{"as_of":"2026-08-15T04:14:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:dd3cec31127ce318c204559ff3d043b3becae9765e24384fab915dff844752b6","coverage":[{"denominator":59,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":59,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T12:38:43.625151Z","state":"measured"},{"denominator":59,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":59,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2505.24172/citation-record","integrity":"/paper/2505.24172/integrity","json":"/paper/2505.24172/citation-record.json","paper":"/paper/2505.24172"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:38:58.360724Z","title":"Item-based collabora- tive filtering recommendation algorithms,","venue":null,"work_id":"aceb384e-d2e1-4a48-93e6-4c3d5783828e","year":2001},"citing_paper":{"arxiv_id":"2505.24172","last_updated":"2025-05-30T03:32:26Z","snapshot_observed_at":"2026-08-12T05:28:32.469188Z","submitted_at":"2025-05-30T03:32:26Z","title":"Heterogeneous Graph Masked Contrastive Learning for Robust Recommendation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:33.153477Z"},"links":{"citing_paper":"/paper/2505.24172"},"observation_digest":"sha256:9ff7ab7a75d93869ee1b3c1d904d6fd93437dd14f7d185c81d76f997ee361e14","observation_id":"637ceaff-1efb-4ac6-8015-fecec308b708","resolution":{"observed_at":"2026-08-07T12:38:58.480535Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:38:33.248817Z","title":"Matrix factorization techniques for recommender systems,","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2505.24172","last_updated":"2025-05-30T03:32:26Z","snapshot_observed_at":"2026-08-12T05:28:32.469188Z","submitted_at":"2025-05-30T03:32:26Z","title":"Heterogeneous Graph Masked Contrastive Learning for Robust Recommendation","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:33.248817Z"},"links":{"citing_paper":"/paper/2505.24172"},"observation_digest":"sha256:6e0120398d9101afb29c730a1d4798fc03304c056e673d31aec9144d3fc098c9","observation_id":"d8504c2c-84fe-4097-a169-882dccd7877e","resolution":{"observed_at":"2026-08-07T12:38:33.248817Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:38:58.039079Z","title":"Neural collaborative filtering,","venue":null,"work_id":"6408262a-7473-4d33-a36a-8e13944add29","year":2017},"citing_paper":{"arxiv_id":"2505.24172","last_updated":"2025-05-30T03:32:26Z","snapshot_observed_at":"2026-08-12T05:28:32.469188Z","submitted_at":"2025-05-30T03:32:26Z","title":"Heterogeneous Graph Masked Contrastive Learning for Robust Recommendation","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:33.401668Z"},"links":{"citing_paper":"/paper/2505.24172"},"observation_digest":"sha256:b9c864067891a8915fca7163beb427011604c2f4d9d6b11d9f0e849c198d0a88","observation_id":"d8059669-5f0c-461d-89db-b6bf3a864b9e","resolution":{"observed_at":"2026-08-07T12:38:58.184208Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:38:57.763690Z","title":"A survey of heterogeneous information network analysis,","venue":null,"work_id":"9c8e4f53-7e4c-478c-9804-cc623c7334fc","year":2016},"citing_paper":{"arxiv_id":"2505.24172","last_updated":"2025-05-30T03:32:26Z","snapshot_observed_at":"2026-08-12T05:28:32.469188Z","submitted_at":"2025-05-30T03:32:26Z","title":"Heterogeneous Graph Masked Contrastive Learning for Robust Recommendation","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:33.559188Z"},"links":{"citing_paper":"/paper/2505.24172"},"observation_digest":"sha256:a3761b3ade37d1b7e97b83fdedb539abe766d1097c059dd0b7b76664def07add","observation_id":"8bf28db8-2768-4ee0-a53e-a93689e9b4d3","resolution":{"observed_at":"2026-08-07T12:38:57.890728Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:38:57.517222Z","title":"A heterogeneous information network based cross domain insurance recommendation system for cold start users,","venue":null,"work_id":"affca03d-4c25-4ea5-93fc-ee0acf9fcedd","year":2020},"citing_paper":{"arxiv_id":"2505.24172","last_updated":"2025-05-30T03:32:26Z","snapshot_observed_at":"2026-08-12T05:28:32.469188Z","submitted_at":"2025-05-30T03:32:26Z","title":"Heterogeneous Graph Masked Contrastive Learning for Robust Recommendation","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:33.645065Z"},"links":{"citing_paper":"/paper/2505.24172"},"observation_digest":"sha256:88534ec88bef3f8ece5d34143489ef2879b8ca0a75d4d510cae05ac8d8bafb4a","observation_id":"e48582b3-2f6c-4c73-a09b-f885246f73fb","resolution":{"observed_at":"2026-08-07T12:38:57.617223Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:38:57.247532Z","title":"Metapath- guided heterogeneous graph neural network for intent recommendation,","venue":null,"work_id":"1edcaaa3-c037-4b5b-9772-210b0efb260d","year":2019},"citing_paper":{"arxiv_id":"2505.24172","last_updated":"2025-05-30T03:32:26Z","snapshot_observed_at":"2026-08-12T05:28:32.469188Z","submitted_at":"2025-05-30T03:32:26Z","title":"Heterogeneous Graph Masked Contrastive Learning for Robust Recommendation","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:33.763492Z"},"links":{"citing_paper":"/paper/2505.24172"},"observation_digest":"sha256:17dd633e497fb4cb66cf3b7822eaea0634d1e3870a88e20ae2aab6adccde3f38","observation_id":"9dbdd899-d02d-4122-b928-0525c4623209","resolution":{"observed_at":"2026-08-07T12:38:57.370124Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:38:56.986971Z","title":"Context- dependent propagating-based video recommendation in multimodal het- erogeneous information networks,","venue":null,"work_id":"fdb6b040-871f-4370-a654-3865ee710313","year":2019},"citing_paper":{"arxiv_id":"2505.24172","last_updated":"2025-05-30T03:32:26Z","snapshot_observed_at":"2026-08-12T05:28:32.469188Z","submitted_at":"2025-05-30T03:32:26Z","title":"Heterogeneous Graph Masked Contrastive Learning for Robust Recommendation","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:34.051951Z"},"links":{"citing_paper":"/paper/2505.24172"},"observation_digest":"sha256:44b022d0a59a8689435f079949dafcf3cdfb11a30c278e07ab6bca0b8e6795e2","observation_id":"9af1025a-672c-4b2d-9d28-d2fef952ceef","resolution":{"observed_at":"2026-08-07T12:38:57.121893Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:38:56.757423Z","title":"Graph attention networks,","venue":null,"work_id":"7ced3e63-b935-4df6-afb2-fd1d230deedf","year":2017},"citing_paper":{"arxiv_id":"2505.24172","last_updated":"2025-05-30T03:32:26Z","snapshot_observed_at":"2026-08-12T05:28:32.469188Z","submitted_at":"2025-05-30T03:32:26Z","title":"Heterogeneous Graph Masked Contrastive Learning for Robust Recommendation","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:36.782816Z"},"links":{"citing_paper":"/paper/2505.24172"},"observation_digest":"sha256:178e0a34a5a2edb15d5df2b38aa80ba2fa293fd8f879326c30b357331fd174a6","observation_id":"53b9cada-5bbc-4b73-96b9-d335b81d4f36","resolution":{"observed_at":"2026-08-07T12:38:56.867453Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1609.02907","last_updated":"2017-02-22T09:55:36Z","snapshot_observed_at":"2026-08-13T11:38:10.906031Z","submitted_at":"2016-09-09T19:48:41Z","title":"Semi-Supervised Classification with Graph Convolutional Networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1609.02907","snapshot_observed_at":"2026-08-07T12:38:36.954876Z","title":"Semi-supervised classification with graph convolutional networks,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2505.24172","last_updated":"2025-05-30T03:32:26Z","snapshot_observed_at":"2026-08-12T05:28:32.469188Z","submitted_at":"2025-05-30T03:32:26Z","title":"Heterogeneous Graph Masked Contrastive Learning for Robust Recommendation","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:36.954876Z"},"links":{"cited_paper":"/paper/1609.02907","citing_paper":"/paper/2505.24172"},"observation_digest":"sha256:4d5dc5a817b6106f62aeac38233544fb9eb2963b794f1ac18ff609e2b4eba72f","observation_id":"ad712f2d-0ffe-429d-8287-65c557ab2f21","resolution":{"observed_at":"2026-08-07T12:38:36.954876Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:38:56.438502Z","title":"Graph random neural networks for semi- supervised learning on graphs,","venue":null,"work_id":"8228a7cd-ad77-464d-8c90-853c953ace96","year":2020},"citing_paper":{"arxiv_id":"2505.24172","last_updated":"2025-05-30T03:32:26Z","snapshot_observed_at":"2026-08-12T05:28:32.469188Z","submitted_at":"2025-05-30T03:32:26Z","title":"Heterogeneous Graph Masked Contrastive Learning for Robust Recommendation","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:37.112102Z"},"links":{"citing_paper":"/paper/2505.24172"},"observation_digest":"sha256:6316bfec30eed407677d0022c792c42635618376666f231dcbdfe80de5bcca0d","observation_id":"a9a0c66f-8d9d-4f39-aeac-89dbcc7e63ac","resolution":{"observed_at":"2026-08-07T12:38:56.566315Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:38:56.095898Z","title":"Lightgcn: Simplifying and powering graph convolution network for recommenda- tion,","venue":null,"work_id":"845f0c4d-165a-451a-b122-3e323305b81b","year":2020},"citing_paper":{"arxiv_id":"2505.24172","last_updated":"2025-05-30T03:32:26Z","snapshot_observed_at":"2026-08-12T05:28:32.469188Z","submitted_at":"2025-05-30T03:32:26Z","title":"Heterogeneous Graph Masked Contrastive Learning for Robust Recommendation","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:37.276473Z"},"links":{"citing_paper":"/paper/2505.24172"},"observation_digest":"sha256:322550bed63cb9c2787a430e4c32c405112ba47d16c8cf43cd67a895d86e19b5","observation_id":"ce794b11-f339-47e2-975e-7dcc37bee496","resolution":{"observed_at":"2026-08-07T12:38:56.274401Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:38:55.858236Z","title":"Neural graph collaborative filtering,","venue":null,"work_id":"77f9a9d4-65c6-4b86-9b18-a5f063f626dc","year":2019},"citing_paper":{"arxiv_id":"2505.24172","last_updated":"2025-05-30T03:32:26Z","snapshot_observed_at":"2026-08-12T05:28:32.469188Z","submitted_at":"2025-05-30T03:32:26Z","title":"Heterogeneous Graph Masked Contrastive Learning for Robust Recommendation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:37.416735Z"},"links":{"citing_paper":"/paper/2505.24172"},"observation_digest":"sha256:a22b0f52586e30609c08bd82ed928d35072c5d3652e243600877431bc7d297be","observation_id":"2fe01148-503b-4611-8c9e-416b3ac2c8e4","resolution":{"observed_at":"2026-08-07T12:38:55.959017Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:38:37.534532Z","title":"Heterogeneous graph attention network,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.24172","last_updated":"2025-05-30T03:32:26Z","snapshot_observed_at":"2026-08-12T05:28:32.469188Z","submitted_at":"2025-05-30T03:32:26Z","title":"Heterogeneous Graph Masked Contrastive Learning for Robust Recommendation","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:37.534532Z"},"links":{"citing_paper":"/paper/2505.24172"},"observation_digest":"sha256:5662b4bb22eab30c21df6ad7a2880d3477f14f005ce516d61f48ee4095419931","observation_id":"26ed8446-7bc9-4ccb-8297-28758b30710c","resolution":{"observed_at":"2026-08-07T12:38:37.534532Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:38:55.579058Z","title":"Self-supervised heterogeneous graph neural network with co-contrastive learning,","venue":null,"work_id":"41c3a993-3b82-405b-bba4-676a1485a79f","year":2021},"citing_paper":{"arxiv_id":"2505.24172","last_updated":"2025-05-30T03:32:26Z","snapshot_observed_at":"2026-08-12T05:28:32.469188Z","submitted_at":"2025-05-30T03:32:26Z","title":"Heterogeneous Graph Masked Contrastive Learning for Robust Recommendation","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:37.678751Z"},"links":{"citing_paper":"/paper/2505.24172"},"observation_digest":"sha256:ba5782c41c0e06f5e36755cef7456249362e8effe7ca685198b80a56acab64c0","observation_id":"4f2a98b7-7c27-4d2c-9553-bf6355f7b769","resolution":{"observed_at":"2026-08-07T12:38:55.688454Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:38:55.327846Z","title":"Robust heterogeneous graph neural networks against adversarial attacks,","venue":null,"work_id":"5bacea99-0e34-4ce2-b229-d519106f371f","year":2022},"citing_paper":{"arxiv_id":"2505.24172","last_updated":"2025-05-30T03:32:26Z","snapshot_observed_at":"2026-08-12T05:28:32.469188Z","submitted_at":"2025-05-30T03:32:26Z","title":"Heterogeneous Graph Masked Contrastive Learning for Robust Recommendation","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:37.801707Z"},"links":{"citing_paper":"/paper/2505.24172"},"observation_digest":"sha256:6ce395cf888a3629ac14a839ffdbabc6f00a06938cb38f486a3e3b03800660de","observation_id":"4fb144dc-34b2-4596-9c94-79bdf5f87ee1","resolution":{"observed_at":"2026-08-07T12:38:55.432750Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:38:37.949901Z","title":"A simple framework for contrastive learning of visual representations,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.24172","last_updated":"2025-05-30T03:32:26Z","snapshot_observed_at":"2026-08-12T05:28:32.469188Z","submitted_at":"2025-05-30T03:32:26Z","title":"Heterogeneous Graph Masked Contrastive Learning for Robust Recommendation","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:37.949901Z"},"links":{"citing_paper":"/paper/2505.24172"},"observation_digest":"sha256:cc365255268c8ca4fd7c62c6bbe47a897fd069c3c2d11fa759303bf2b4959259","observation_id":"c0653d9b-b857-471b-8585-359345fed3f8","resolution":{"observed_at":"2026-08-07T12:38:37.949901Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:38:55.066651Z","title":"Bootstrap your own latent-a new approach to self-supervised learning,","venue":null,"work_id":"857434b0-3119-4281-9324-f80570f4a4e0","year":2020},"citing_paper":{"arxiv_id":"2505.24172","last_updated":"2025-05-30T03:32:26Z","snapshot_observed_at":"2026-08-12T05:28:32.469188Z","submitted_at":"2025-05-30T03:32:26Z","title":"Heterogeneous Graph Masked Contrastive Learning for Robust Recommendation","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:38.084446Z"},"links":{"citing_paper":"/paper/2505.24172"},"observation_digest":"sha256:22380c398a2c2e70084c952fc7ad32da56e358d94ef7e620fbf1a81b4126a01d","observation_id":"2ad41b36-0e7e-4405-8129-08119e34270f","resolution":{"observed_at":"2026-08-07T12:38:55.169763Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:38:54.781841Z","title":"Heterogeneous graph contrastive learning for recommendation,","venue":null,"work_id":"5b5e0d23-86ee-445a-8e65-e44209487f66","year":2023},"citing_paper":{"arxiv_id":"2505.24172","last_updated":"2025-05-30T03:32:26Z","snapshot_observed_at":"2026-08-12T05:28:32.469188Z","submitted_at":"2025-05-30T03:32:26Z","title":"Heterogeneous Graph Masked Contrastive Learning for Robust Recommendation","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:38.261563Z"},"links":{"citing_paper":"/paper/2505.24172"},"observation_digest":"sha256:42784959bf57a860a8c88bfccd5fe22972a9a3b67b5f5d0483aec8082e7292ce","observation_id":"de481d07-a9e7-4cca-9e09-fa5134801fc0","resolution":{"observed_at":"2026-08-07T12:38:54.928808Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:38:54.494088Z","title":"Self- supervised graph learning for recommendation,","venue":null,"work_id":"2a130b37-065b-4bc3-8578-f9829098c1b1","year":2021},"citing_paper":{"arxiv_id":"2505.24172","last_updated":"2025-05-30T03:32:26Z","snapshot_observed_at":"2026-08-12T05:28:32.469188Z","submitted_at":"2025-05-30T03:32:26Z","title":"Heterogeneous Graph Masked Contrastive Learning for Robust Recommendation","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:38.403691Z"},"links":{"citing_paper":"/paper/2505.24172"},"observation_digest":"sha256:603c31eccc5e338d9c5c09259437dbc88dd4ec7486e923a4e575f85c8e829ab9","observation_id":"b43e12ef-7d8f-4793-b411-571307a90647","resolution":{"observed_at":"2026-08-07T12:38:54.625775Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1807.03748","last_updated":"2019-01-22T18:47:12Z","snapshot_observed_at":"2026-08-14T18:53:38.574749Z","submitted_at":"2018-07-10T16:52:11Z","title":"Representation Learning with Contrastive Predictive Coding","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1807.03748","snapshot_observed_at":"2026-08-07T12:38:38.561818Z","title":"Representation learning with contrastive predictive coding,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.24172","last_updated":"2025-05-30T03:32:26Z","snapshot_observed_at":"2026-08-12T05:28:32.469188Z","submitted_at":"2025-05-30T03:32:26Z","title":"Heterogeneous Graph Masked Contrastive Learning for Robust Recommendation","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:38.561818Z"},"links":{"cited_paper":"/paper/1807.03748","citing_paper":"/paper/2505.24172"},"observation_digest":"sha256:b50562317b30c1e02c84b24fd7bac3d23c52c84969e0ff983a6d5ce265b1e355","observation_id":"54670e80-83b5-4ee3-a18d-35af8f235f4c","resolution":{"observed_at":"2026-08-07T12:38:38.561818Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:38:54.167174Z","title":"Heterogeneous graph contrastive learning network for personalized micro-video recom- mendation,","venue":null,"work_id":"0587466d-79a6-4fb3-82d4-8e784145065b","year":2022},"citing_paper":{"arxiv_id":"2505.24172","last_updated":"2025-05-30T03:32:26Z","snapshot_observed_at":"2026-08-12T05:28:32.469188Z","submitted_at":"2025-05-30T03:32:26Z","title":"Heterogeneous Graph Masked Contrastive Learning for Robust Recommendation","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:38.688931Z"},"links":{"citing_paper":"/paper/2505.24172"},"observation_digest":"sha256:c52c44759fbd993fd8352e472322f3088a02e81cbce88597ad4984b3ab54938b","observation_id":"d3557914-40a0-4e41-8e5e-cb2015f4319c","resolution":{"observed_at":"2026-08-07T12:38:54.330193Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:38:53.835956Z","title":"Heterogeneous informa- tion network embedding for recommendation,","venue":null,"work_id":"45a581f9-5cde-4124-9924-ecd8e6ca9387","year":2018},"citing_paper":{"arxiv_id":"2505.24172","last_updated":"2025-05-30T03:32:26Z","snapshot_observed_at":"2026-08-12T05:28:32.469188Z","submitted_at":"2025-05-30T03:32:26Z","title":"Heterogeneous Graph Masked Contrastive Learning for Robust Recommendation","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:38.822101Z"},"links":{"citing_paper":"/paper/2505.24172"},"observation_digest":"sha256:69c7686116dce42d053872ff5abdcfdf1d8cea6f1281093d2d2d7755a4aeb912","observation_id":"e3fa71e3-4cf3-44ed-944d-733f65aac549","resolution":{"observed_at":"2026-08-07T12:38:54.002690Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:38:53.597068Z","title":"Social recommendation with self-supervised metagraph informax network,","venue":null,"work_id":"26b47496-b87c-488a-a67c-21849977875d","year":2021},"citing_paper":{"arxiv_id":"2505.24172","last_updated":"2025-05-30T03:32:26Z","snapshot_observed_at":"2026-08-12T05:28:32.469188Z","submitted_at":"2025-05-30T03:32:26Z","title":"Heterogeneous Graph Masked Contrastive Learning for Robust Recommendation","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:38.948933Z"},"links":{"citing_paper":"/paper/2505.24172"},"observation_digest":"sha256:adf396c4474d7bd4bc99ad668b2e718c33fb212355f68875f91c3b2ccd3ddd6a","observation_id":"a77605f4-048e-488f-b7b1-e0bd864cb77a","resolution":{"observed_at":"2026-08-07T12:38:53.708477Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:38:53.263246Z","title":"Are graph augmentations necessary? simple graph contrastive learning for recommendation,","venue":null,"work_id":"05de3607-4967-41f7-983f-f2234db3e01f","year":2022},"citing_paper":{"arxiv_id":"2505.24172","last_updated":"2025-05-30T03:32:26Z","snapshot_observed_at":"2026-08-12T05:28:32.469188Z","submitted_at":"2025-05-30T03:32:26Z","title":"Heterogeneous Graph Masked Contrastive Learning for Robust Recommendation","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:39.064470Z"},"links":{"citing_paper":"/paper/2505.24172"},"observation_digest":"sha256:4b99a881d955292d418d43a85872bc677d580ce215404f174fc72d74895811cc","observation_id":"240816a2-b859-478a-82ab-d735e37dfc2d","resolution":{"observed_at":"2026-08-07T12:38:53.403960Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:38:53.020639Z","title":"Pathsim: Meta path-based top-k similarity search in heterogeneous information networks,","venue":null,"work_id":"043f8fdd-1b07-4ae4-9632-e6bcab8bffb4","year":2011},"citing_paper":{"arxiv_id":"2505.24172","last_updated":"2025-05-30T03:32:26Z","snapshot_observed_at":"2026-08-12T05:28:32.469188Z","submitted_at":"2025-05-30T03:32:26Z","title":"Heterogeneous Graph Masked Contrastive Learning for Robust Recommendation","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:39.181492Z"},"links":{"citing_paper":"/paper/2505.24172"},"observation_digest":"sha256:f9adc62e59c795d33ca67bb5006e2cc9bba392907d270c5ab9a977eb9f43bd02","observation_id":"0e64c245-fe76-4646-b172-3f4753965dd4","resolution":{"observed_at":"2026-08-07T12:38:53.153972Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:38:52.807779Z","title":"Understanding the difficulty of training deep feedforward neural networks,","venue":null,"work_id":"38f10399-115b-49fa-a1d3-4efff781a7ae","year":2010},"citing_paper":{"arxiv_id":"2505.24172","last_updated":"2025-05-30T03:32:26Z","snapshot_observed_at":"2026-08-12T05:28:32.469188Z","submitted_at":"2025-05-30T03:32:26Z","title":"Heterogeneous Graph Masked Contrastive Learning for Robust Recommendation","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:39.342717Z"},"links":{"citing_paper":"/paper/2505.24172"},"observation_digest":"sha256:ae1b23e98f87d3832cfa4a39df48935a797bcdbe599f22ab5674f24f0950937d","observation_id":"14ade10d-ea0b-460f-9683-a9f1b5948bcc","resolution":{"observed_at":"2026-08-07T12:38:52.916437Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:38:52.596147Z","title":"Birds of a feather: Homophily in social networks,","venue":null,"work_id":"ad5e728d-f143-45e2-86f3-6e2c544813ff","year":2001},"citing_paper":{"arxiv_id":"2505.24172","last_updated":"2025-05-30T03:32:26Z","snapshot_observed_at":"2026-08-12T05:28:32.469188Z","submitted_at":"2025-05-30T03:32:26Z","title":"Heterogeneous Graph Masked Contrastive Learning for Robust Recommendation","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:39.470575Z"},"links":{"citing_paper":"/paper/2505.24172"},"observation_digest":"sha256:d945d38e743c478386794ab972afb5c3d0599caa9c78c93f1092f0f5dfcca546","observation_id":"8856f4fb-94cf-4394-80b9-2c310dbccfb1","resolution":{"observed_at":"2026-08-07T12:38:52.705011Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1205.2618","last_updated":"2012-05-09T18:25:09Z","snapshot_observed_at":"2026-08-15T03:23:25.220336Z","submitted_at":"2012-05-09T18:25:09Z","title":"BPR: Bayesian Personalized Ranking from Implicit Feedback","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1205.2618","snapshot_observed_at":"2026-08-07T12:38:39.577145Z","title":"Bpr: Bayesian personalized ranking from implicit feedback,","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2505.24172","last_updated":"2025-05-30T03:32:26Z","snapshot_observed_at":"2026-08-12T05:28:32.469188Z","submitted_at":"2025-05-30T03:32:26Z","title":"Heterogeneous Graph Masked Contrastive Learning for Robust Recommendation","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:39.577145Z"},"links":{"cited_paper":"/paper/1205.2618","citing_paper":"/paper/2505.24172"},"observation_digest":"sha256:071912351f495eafaf02e3395cee8819e7e91bc71c7a7179e9791f597e858a09","observation_id":"e0813ca9-9b1b-4a37-ab17-5174ad7425ba","resolution":{"observed_at":"2026-08-07T12:38:39.577145Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:38:52.332234Z","title":"Heterogeneous graph trans- former,","venue":null,"work_id":"3c147032-84aa-4cb4-86c0-71a55c12343a","year":2020},"citing_paper":{"arxiv_id":"2505.24172","last_updated":"2025-05-30T03:32:26Z","snapshot_observed_at":"2026-08-12T05:28:32.469188Z","submitted_at":"2025-05-30T03:32:26Z","title":"Heterogeneous Graph Masked Contrastive Learning for Robust Recommendation","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:39.721127Z"},"links":{"citing_paper":"/paper/2505.24172"},"observation_digest":"sha256:6767e5034f87163414fd8c5b76581acfc079d5ea61d61962cce0655471b8f746","observation_id":"4515ff88-b97f-429c-8875-e07c6695bb1a","resolution":{"observed_at":"2026-08-07T12:38:52.428193Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:38:52.080262Z","title":"Kgat: Knowledge graph attention network for recommendation,","venue":null,"work_id":"d63418e2-10de-4af3-a467-6ac74c5c6842","year":2019},"citing_paper":{"arxiv_id":"2505.24172","last_updated":"2025-05-30T03:32:26Z","snapshot_observed_at":"2026-08-12T05:28:32.469188Z","submitted_at":"2025-05-30T03:32:26Z","title":"Heterogeneous Graph Masked Contrastive Learning for Robust Recommendation","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:39.857355Z"},"links":{"citing_paper":"/paper/2505.24172"},"observation_digest":"sha256:bc9d948616e23d365e4fd1fa6a53cb6451952ae62b6318200b050b820f2d39cb","observation_id":"bce98896-aa95-45d0-80c8-18efde479c89","resolution":{"observed_at":"2026-08-07T12:38:52.208947Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:38:51.813453Z","title":"Openhgnn: An open source toolkit for heterogeneous graph neural network,","venue":null,"work_id":"b7ca888a-247b-44e5-9bdd-2e59dabfd128","year":2022},"citing_paper":{"arxiv_id":"2505.24172","last_updated":"2025-05-30T03:32:26Z","snapshot_observed_at":"2026-08-12T05:28:32.469188Z","submitted_at":"2025-05-30T03:32:26Z","title":"Heterogeneous Graph Masked Contrastive Learning for Robust Recommendation","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:40.006078Z"},"links":{"citing_paper":"/paper/2505.24172"},"observation_digest":"sha256:68c2549db1e8602210931f2e30e715340283aa11031d7b332aa34ae1199f53ea","observation_id":"c14e2a4d-5e09-4e15-bdd9-be7968e241c7","resolution":{"observed_at":"2026-08-07T12:38:51.938441Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:38:51.477270Z","title":"Leveraging meta-path based context for top-n recommendation with a neural co-attention model,","venue":null,"work_id":"0a1e10cc-8f56-408b-9562-3c2010bd3df6","year":2018},"citing_paper":{"arxiv_id":"2505.24172","last_updated":"2025-05-30T03:32:26Z","snapshot_observed_at":"2026-08-12T05:28:32.469188Z","submitted_at":"2025-05-30T03:32:26Z","title":"Heterogeneous Graph Masked Contrastive Learning for Robust Recommendation","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:40.135510Z"},"links":{"citing_paper":"/paper/2505.24172"},"observation_digest":"sha256:3186bca139e92c004be7b73a51afda448d446ba79ec7ea6d7951e2d1cc4a5958","observation_id":"a7411ea3-0926-460a-b5e6-915adcfb92fe","resolution":{"observed_at":"2026-08-07T12:38:51.628733Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:38:51.240124Z","title":"Robust preference-guided based disentangled graph social rec- ommendation,","venue":null,"work_id":"d3592866-578f-4779-ab91-7fae44b47a8c","year":2024},"citing_paper":{"arxiv_id":"2505.24172","last_updated":"2025-05-30T03:32:26Z","snapshot_observed_at":"2026-08-12T05:28:32.469188Z","submitted_at":"2025-05-30T03:32:26Z","title":"Heterogeneous Graph Masked Contrastive Learning for Robust Recommendation","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:40.287173Z"},"links":{"citing_paper":"/paper/2505.24172"},"observation_digest":"sha256:24176e60beb4262ff7315c27e00768dab235dd7304156d1a2b297ca67bfd17b8","observation_id":"21cddae6-8fdb-4c73-9c6c-b3d39e4a6d50","resolution":{"observed_at":"2026-08-07T12:38:51.352954Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:38:50.971965Z","title":"Visualizing data using t-sne","venue":null,"work_id":"84f906a4-b74c-47d6-b8e0-046f1d582077","year":2008},"citing_paper":{"arxiv_id":"2505.24172","last_updated":"2025-05-30T03:32:26Z","snapshot_observed_at":"2026-08-12T05:28:32.469188Z","submitted_at":"2025-05-30T03:32:26Z","title":"Heterogeneous Graph Masked Contrastive Learning for Robust Recommendation","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:40.444342Z"},"links":{"citing_paper":"/paper/2505.24172"},"observation_digest":"sha256:5111954c1ad12ae1e3c6688f3ef55715baef015b8419825a1afe04c109ac0718","observation_id":"12251469-7e0b-48b5-8b18-21477c3f037d","resolution":{"observed_at":"2026-08-07T12:38:51.095640Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:38:50.664933Z","title":"Understanding contrastive representation learning through alignment and uniformity on the hypersphere,","venue":null,"work_id":"3f24a908-e94a-4c94-8084-7cd9a5a860c9","year":2020},"citing_paper":{"arxiv_id":"2505.24172","last_updated":"2025-05-30T03:32:26Z","snapshot_observed_at":"2026-08-12T05:28:32.469188Z","submitted_at":"2025-05-30T03:32:26Z","title":"Heterogeneous Graph Masked Contrastive Learning for Robust Recommendation","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:40.610072Z"},"links":{"citing_paper":"/paper/2505.24172"},"observation_digest":"sha256:d7d6abc67dd8c49a6e59c022ae10cc6116efcc9aee7750ae00a29fc9f46ae21c","observation_id":"7df5a08a-1039-4606-ad51-c2e169d1e335","resolution":{"observed_at":"2026-08-07T12:38:50.814202Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:38:50.448069Z","title":"Graph neural networks for social recommendation,","venue":null,"work_id":"3fac37ae-7a0e-4a58-9d37-4cc317d8aad5","year":2019},"citing_paper":{"arxiv_id":"2505.24172","last_updated":"2025-05-30T03:32:26Z","snapshot_observed_at":"2026-08-12T05:28:32.469188Z","submitted_at":"2025-05-30T03:32:26Z","title":"Heterogeneous Graph Masked Contrastive Learning for Robust Recommendation","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:40.706672Z"},"links":{"citing_paper":"/paper/2505.24172"},"observation_digest":"sha256:2c917e5bbd23a6c9d894b0ebf7875c05c17c039ffca359bbb629ef45c220f638","observation_id":"b175b948-18d0-4611-af29-1742fd07d7bf","resolution":{"observed_at":"2026-08-07T12:38:50.561561Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:38:50.172185Z","title":"Modeling relational data with graph convolutional networks,","venue":null,"work_id":"4c9f3f2f-d032-49f2-bcd5-ef56673c7d66","year":2018},"citing_paper":{"arxiv_id":"2505.24172","last_updated":"2025-05-30T03:32:26Z","snapshot_observed_at":"2026-08-12T05:28:32.469188Z","submitted_at":"2025-05-30T03:32:26Z","title":"Heterogeneous Graph Masked Contrastive Learning for Robust Recommendation","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:40.846675Z"},"links":{"citing_paper":"/paper/2505.24172"},"observation_digest":"sha256:83163cb4ddf12786fd59c4fdb4d436d167f21eced51401cd5cc3eab02e5fba27","observation_id":"0405a278-101b-4aa7-a207-18cbe93d9ecd","resolution":{"observed_at":"2026-08-07T12:38:50.318201Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:38:49.812073Z","title":"Graph-refined convolutional network for multimedia recommendation with implicit feedback,","venue":null,"work_id":"c1663ae0-80b1-45e4-be75-661c92e6f4fe","year":2020},"citing_paper":{"arxiv_id":"2505.24172","last_updated":"2025-05-30T03:32:26Z","snapshot_observed_at":"2026-08-12T05:28:32.469188Z","submitted_at":"2025-05-30T03:32:26Z","title":"Heterogeneous Graph Masked Contrastive Learning for Robust Recommendation","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:40.987685Z"},"links":{"citing_paper":"/paper/2505.24172"},"observation_digest":"sha256:231b870c43a903574f0c2cee1049bb3052bac76648d89d97766d59bfcbdd1e9c","observation_id":"fe951c8a-c844-4079-bd87-047d1de82204","resolution":{"observed_at":"2026-08-07T12:38:49.977221Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1810.04805","last_updated":"2019-05-24T20:37:26Z","snapshot_observed_at":"2026-08-14T18:16:28.847993Z","submitted_at":"2018-10-11T00:50:01Z","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.04805","snapshot_observed_at":"2026-08-07T12:38:41.162588Z","title":"Bert: Pre-training of deep bidirectional transformers for language understanding,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.24172","last_updated":"2025-05-30T03:32:26Z","snapshot_observed_at":"2026-08-12T05:28:32.469188Z","submitted_at":"2025-05-30T03:32:26Z","title":"Heterogeneous Graph Masked Contrastive Learning for Robust Recommendation","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:41.162588Z"},"links":{"cited_paper":"/paper/1810.04805","citing_paper":"/paper/2505.24172"},"observation_digest":"sha256:44c52d89d9e1d6429f74bb73f0d0ca57ebdee9df4230be430404766944a51655","observation_id":"d842c58f-f9fa-41a2-96b3-d4e022154547","resolution":{"observed_at":"2026-08-07T12:38:41.162588Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:38:49.523632Z","title":"Distributed representations of words and phrases and their composi- tionality,","venue":null,"work_id":"9ecd0f40-16d8-4307-8193-9a687b740abc","year":2013},"citing_paper":{"arxiv_id":"2505.24172","last_updated":"2025-05-30T03:32:26Z","snapshot_observed_at":"2026-08-12T05:28:32.469188Z","submitted_at":"2025-05-30T03:32:26Z","title":"Heterogeneous Graph Masked Contrastive Learning for Robust Recommendation","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:41.290824Z"},"links":{"citing_paper":"/paper/2505.24172"},"observation_digest":"sha256:9071f5a8705b17506fae2cc6c16bf3ee57c53f35bdb2416099abcd5b77bd08bc","observation_id":"df8a70f2-bf72-4d57-9ada-9db2c3eb036f","resolution":{"observed_at":"2026-08-07T12:38:49.672715Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:38:49.216501Z","title":"Multi-behavior sequential recommendation with temporal graph transformer,","venue":null,"work_id":"86a636d3-4bdd-4389-a371-a6fb3563c75b","year":2022},"citing_paper":{"arxiv_id":"2505.24172","last_updated":"2025-05-30T03:32:26Z","snapshot_observed_at":"2026-08-12T05:28:32.469188Z","submitted_at":"2025-05-30T03:32:26Z","title":"Heterogeneous Graph Masked Contrastive Learning for Robust Recommendation","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:41.409499Z"},"links":{"citing_paper":"/paper/2505.24172"},"observation_digest":"sha256:d39c6d142278d8cc985c2f9061896ff523b7b3f3abf8d552476f80273ab9f8f6","observation_id":"2a233589-9113-4de3-98d8-f4f2da36d71e","resolution":{"observed_at":"2026-08-07T12:38:49.356379Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:38:48.887038Z","title":"News recommendation via multi-interest news sequence modelling,","venue":null,"work_id":"54d15b58-acf1-4b10-8077-ba992fefd820","year":2022},"citing_paper":{"arxiv_id":"2505.24172","last_updated":"2025-05-30T03:32:26Z","snapshot_observed_at":"2026-08-12T05:28:32.469188Z","submitted_at":"2025-05-30T03:32:26Z","title":"Heterogeneous Graph Masked Contrastive Learning for Robust Recommendation","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:41.537741Z"},"links":{"citing_paper":"/paper/2505.24172"},"observation_digest":"sha256:84aae7457518350f97393e28b18372894643e0ab621a9dd6f3e684a885b9d5f8","observation_id":"ead63f0e-6f8a-4298-b674-a7c702cb7fef","resolution":{"observed_at":"2026-08-07T12:38:49.039075Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:38:48.574945Z","title":"Heterogeneous network representation learning: A unified framework with survey and benchmark,","venue":null,"work_id":"18405fd4-fcdb-44b0-99ee-d9991e314234","year":2020},"citing_paper":{"arxiv_id":"2505.24172","last_updated":"2025-05-30T03:32:26Z","snapshot_observed_at":"2026-08-12T05:28:32.469188Z","submitted_at":"2025-05-30T03:32:26Z","title":"Heterogeneous Graph Masked Contrastive Learning for Robust Recommendation","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:41.663557Z"},"links":{"citing_paper":"/paper/2505.24172"},"observation_digest":"sha256:2303c397d7eb3b0132294c84d94010dde9027514fe0c32a1b1b50e43589371a9","observation_id":"a04872ba-0d9c-4c4b-b36f-e1e1c6341519","resolution":{"observed_at":"2026-08-07T12:38:48.714222Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:38:48.314521Z","title":"User- context collaboration and tensor factorization for gnn-based social rec- ommendation,","venue":null,"work_id":"a0f1b7fc-d000-4989-ad74-6652a561fb29","year":2023},"citing_paper":{"arxiv_id":"2505.24172","last_updated":"2025-05-30T03:32:26Z","snapshot_observed_at":"2026-08-12T05:28:32.469188Z","submitted_at":"2025-05-30T03:32:26Z","title":"Heterogeneous Graph Masked Contrastive Learning for Robust Recommendation","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:41.836603Z"},"links":{"citing_paper":"/paper/2505.24172"},"observation_digest":"sha256:ef4253602113559960691dc1ee61e1050fabe22e35b70cb9d82ae1ab9fd3982a","observation_id":"6378728f-6395-4259-bb0b-c59a7a81b95e","resolution":{"observed_at":"2026-08-07T12:38:48.454950Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:38:48.020516Z","title":"Self-attentive graph convo- lution network with latent group mining and collaborative filtering for personalized recommendation,","venue":null,"work_id":"7159c052-b7e8-4ef0-8937-ff6ecc993c09","year":2021},"citing_paper":{"arxiv_id":"2505.24172","last_updated":"2025-05-30T03:32:26Z","snapshot_observed_at":"2026-08-12T05:28:32.469188Z","submitted_at":"2025-05-30T03:32:26Z","title":"Heterogeneous Graph Masked Contrastive Learning for Robust Recommendation","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:41.975535Z"},"links":{"citing_paper":"/paper/2505.24172"},"observation_digest":"sha256:9cd78195da2e752ad03e6d50e4a59b5d91b5c6801f34332575ee174e4fdf31ca","observation_id":"82cefb50-b278-4713-981a-f549f9cd614b","resolution":{"observed_at":"2026-08-07T12:38:48.143969Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:38:47.718279Z","title":"Meta-path based neighbors for behavioral target generalization in sequential recommendation,","venue":null,"work_id":"506b889f-8f0b-4b25-9797-60be4d2debf5","year":2022},"citing_paper":{"arxiv_id":"2505.24172","last_updated":"2025-05-30T03:32:26Z","snapshot_observed_at":"2026-08-12T05:28:32.469188Z","submitted_at":"2025-05-30T03:32:26Z","title":"Heterogeneous Graph Masked Contrastive Learning for Robust Recommendation","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:42.131545Z"},"links":{"citing_paper":"/paper/2505.24172"},"observation_digest":"sha256:a40c6d1bef38b15462d48e7ba25ab227fa86bbc34b165fb222ad410eba9d4739","observation_id":"294f4093-1210-4cf0-ac2f-c9a29a1642bf","resolution":{"observed_at":"2026-08-07T12:38:47.860171Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:38:47.405364Z","title":"Heterogeneous graph structure learning for graph neural networks,","venue":null,"work_id":"235420b2-b61f-4f9a-8c62-3da7851ac14d","year":2021},"citing_paper":{"arxiv_id":"2505.24172","last_updated":"2025-05-30T03:32:26Z","snapshot_observed_at":"2026-08-12T05:28:32.469188Z","submitted_at":"2025-05-30T03:32:26Z","title":"Heterogeneous Graph Masked Contrastive Learning for Robust Recommendation","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:42.227898Z"},"links":{"citing_paper":"/paper/2505.24172"},"observation_digest":"sha256:4d6d645b4dc4232e605454aca4244d90496a763e310866993ac6e14cf41ba499","observation_id":"6400d51c-102c-4ccb-9dac-cb92bd1a8157","resolution":{"observed_at":"2026-08-07T12:38:47.558210Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:38:47.138837Z","title":"Sparse graph attention networks,","venue":null,"work_id":"9ea7e1a5-b33e-48f2-a265-a90a13e8c0db","year":2021},"citing_paper":{"arxiv_id":"2505.24172","last_updated":"2025-05-30T03:32:26Z","snapshot_observed_at":"2026-08-12T05:28:32.469188Z","submitted_at":"2025-05-30T03:32:26Z","title":"Heterogeneous Graph Masked Contrastive Learning for Robust Recommendation","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:42.388174Z"},"links":{"citing_paper":"/paper/2505.24172"},"observation_digest":"sha256:4eaad3f22b9f84d578f4c5ae3d149445ca8d95ccf43ec20801340bbed6cc7339","observation_id":"d4ac933b-30c5-48ca-a8f8-58df73c17ff7","resolution":{"observed_at":"2026-08-07T12:38:47.262004Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:38:46.866103Z","title":"Hgate: heterogeneous graph attention auto-encoders,","venue":null,"work_id":"23c478b2-1790-4395-bb1f-a267381c038b","year":2021},"citing_paper":{"arxiv_id":"2505.24172","last_updated":"2025-05-30T03:32:26Z","snapshot_observed_at":"2026-08-12T05:28:32.469188Z","submitted_at":"2025-05-30T03:32:26Z","title":"Heterogeneous Graph Masked Contrastive Learning for Robust Recommendation","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:42.434349Z"},"links":{"citing_paper":"/paper/2505.24172"},"observation_digest":"sha256:e013db296e83097a6aa312d702d01051f1d38379801c39d068761a44a40aac40","observation_id":"b737d718-fafe-4df5-8773-858178e7c8dd","resolution":{"observed_at":"2026-08-07T12:38:47.007631Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:38:46.508891Z","title":"Towards robust neural graph col- laborative filtering via structure denoising and embedding perturbation,","venue":null,"work_id":"fc86834f-77f5-4a32-a049-d38aa277a9f0","year":2023},"citing_paper":{"arxiv_id":"2505.24172","last_updated":"2025-05-30T03:32:26Z","snapshot_observed_at":"2026-08-12T05:28:32.469188Z","submitted_at":"2025-05-30T03:32:26Z","title":"Heterogeneous Graph Masked Contrastive Learning for Robust Recommendation","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:42.563401Z"},"links":{"citing_paper":"/paper/2505.24172"},"observation_digest":"sha256:5aff63a9be442fbed0582904fe565243eb57bd27cc7b32ffdbf9c968d85341f6","observation_id":"b3484a78-ccc6-4914-8924-a70f74d6dee0","resolution":{"observed_at":"2026-08-07T12:38:46.705345Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:38:46.136465Z","title":"Fedrecattack: Model poisoning attack to federated recommendation,","venue":null,"work_id":"cffa4897-6f99-4649-808d-347d6a29fab0","year":2022},"citing_paper":{"arxiv_id":"2505.24172","last_updated":"2025-05-30T03:32:26Z","snapshot_observed_at":"2026-08-12T05:28:32.469188Z","submitted_at":"2025-05-30T03:32:26Z","title":"Heterogeneous Graph Masked Contrastive Learning for Robust Recommendation","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:42.696158Z"},"links":{"citing_paper":"/paper/2505.24172"},"observation_digest":"sha256:1104d90a16e16d3abf5552b764c4f3ce9935eac72fc98b57345c65e12d3131f7","observation_id":"ac34cc83-9c98-4a56-a912-4767f534beca","resolution":{"observed_at":"2026-08-07T12:38:46.347388Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:38:45.863111Z","title":"Revisiting graph- based recommender systems from the perspective of variational auto- encoder,","venue":null,"work_id":"025e1dbb-14f9-43f8-b37f-785f0558dae3","year":2023},"citing_paper":{"arxiv_id":"2505.24172","last_updated":"2025-05-30T03:32:26Z","snapshot_observed_at":"2026-08-12T05:28:32.469188Z","submitted_at":"2025-05-30T03:32:26Z","title":"Heterogeneous Graph Masked Contrastive Learning for Robust Recommendation","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:42.795705Z"},"links":{"citing_paper":"/paper/2505.24172"},"observation_digest":"sha256:c9d30294432e7f132d3ce60ed02c4caf5fce26e760f355efed0634e4ebb2984c","observation_id":"e13c2dd5-3c19-4e13-9a2e-ad51ee20d97c","resolution":{"observed_at":"2026-08-07T12:38:45.990954Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:38:45.558602Z","title":"Deep matrix factorization with implicit feedback embedding for recommenda- tion system,","venue":null,"work_id":"6784302a-42ba-489d-a719-4e94a8c26ae6","year":2019},"citing_paper":{"arxiv_id":"2505.24172","last_updated":"2025-05-30T03:32:26Z","snapshot_observed_at":"2026-08-12T05:28:32.469188Z","submitted_at":"2025-05-30T03:32:26Z","title":"Heterogeneous Graph Masked Contrastive Learning for Robust Recommendation","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:42.877881Z"},"links":{"citing_paper":"/paper/2505.24172"},"observation_digest":"sha256:e4bb820b7183941b3db484ec02ed55839fc7c10dc279b5af3ed7d7a4f6442fc3","observation_id":"86fe7961-12a2-497b-8107-0eaf2e9c061c","resolution":{"observed_at":"2026-08-07T12:38:45.700138Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:38:45.222792Z","title":"Intent-guided heterogeneous graph contrastive learning for recommendation,","venue":null,"work_id":"a6fed98c-c1ff-4422-b2e6-8b6531cc1065","year":1915},"citing_paper":{"arxiv_id":"2505.24172","last_updated":"2025-05-30T03:32:26Z","snapshot_observed_at":"2026-08-12T05:28:32.469188Z","submitted_at":"2025-05-30T03:32:26Z","title":"Heterogeneous Graph Masked Contrastive Learning for Robust Recommendation","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:42.998202Z"},"links":{"citing_paper":"/paper/2505.24172"},"observation_digest":"sha256:b323b25576cba2ffb5c8fed82f0a3ae53db594d9dce5d4e34e9648e930fd21a4","observation_id":"458916d4-f300-4ad6-b127-94121eff9a6f","resolution":{"observed_at":"2026-08-07T12:38:45.377934Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:38:44.928728Z","title":"Denoising heterogeneous graph pre-training framework for recommendation,","venue":null,"work_id":"b9f029e6-26bc-4838-bf51-c24231b1762c","year":2024},"citing_paper":{"arxiv_id":"2505.24172","last_updated":"2025-05-30T03:32:26Z","snapshot_observed_at":"2026-08-12T05:28:32.469188Z","submitted_at":"2025-05-30T03:32:26Z","title":"Heterogeneous Graph Masked Contrastive Learning for Robust Recommendation","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:43.096525Z"},"links":{"citing_paper":"/paper/2505.24172"},"observation_digest":"sha256:5eb12893fccaedd32fd311645c4e3343999e4f42f95df8e40a65a37e0b3ecc3a","observation_id":"33f53876-f500-4388-9f43-94ea8da20a98","resolution":{"observed_at":"2026-08-07T12:38:45.071811Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:38:44.608380Z","title":"Generative- contrastive heterogeneous graph neural network,","venue":null,"work_id":"eb0cc587-adbd-4328-98fd-d7a9d03befe5","year":2025},"citing_paper":{"arxiv_id":"2505.24172","last_updated":"2025-05-30T03:32:26Z","snapshot_observed_at":"2026-08-12T05:28:32.469188Z","submitted_at":"2025-05-30T03:32:26Z","title":"Heterogeneous Graph Masked Contrastive Learning for Robust Recommendation","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:43.190203Z"},"links":{"citing_paper":"/paper/2505.24172"},"observation_digest":"sha256:e1ec2d8bf43371c479670558a49c05ce6eef0f8690d03714b01714177d8ea1ab","observation_id":"3efbad86-58dd-403e-a6ed-0984cad16565","resolution":{"observed_at":"2026-08-07T12:38:44.773428Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.03307","last_updated":"2025-04-09T07:21:18Z","snapshot_observed_at":"2026-08-14T04:45:24.955295Z","submitted_at":"2025-02-05T16:08:05Z","title":"Intent Representation Learning with Large Language Model for Recommendation","version":4},"cited_work":{"arxiv_id":"2502.03307","doi":null,"metadata_source":"pith","pith_arxiv_id":"2502.03307","snapshot_observed_at":"2026-08-07T12:38:44.080960Z","title":"Intent Representation Learning with Large Language Model for Recommendation","venue":"cs.IR","work_id":"39429bf6-63a8-45ca-b1be-088b0d2738ff","year":2025},"citing_paper":{"arxiv_id":"2505.24172","last_updated":"2025-05-30T03:32:26Z","snapshot_observed_at":"2026-08-12T05:28:32.469188Z","submitted_at":"2025-05-30T03:32:26Z","title":"Heterogeneous Graph Masked Contrastive Learning for Robust Recommendation","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:43.340576Z"},"links":{"cited_paper":"/paper/2502.03307","citing_paper":"/paper/2505.24172"},"observation_digest":"sha256:915eb7c2fb7a3ce51165db485d85a59e3786042f4c375e9517c57cc340649d4d","observation_id":"1c36bec2-bc86-4c75-b932-4a72101a1988","resolution":{"observed_at":"2026-08-07T12:38:44.176489Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:38:44.371250Z","title":"Simple yet effective heterogeneous graph contrastive learning for recommendation,","venue":null,"work_id":"d364c236-d154-4623-a850-30c4b99e4c4b","year":2025},"citing_paper":{"arxiv_id":"2505.24172","last_updated":"2025-05-30T03:32:26Z","snapshot_observed_at":"2026-08-12T05:28:32.469188Z","submitted_at":"2025-05-30T03:32:26Z","title":"Heterogeneous Graph Masked Contrastive Learning for Robust Recommendation","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:43.442977Z"},"links":{"citing_paper":"/paper/2505.24172"},"observation_digest":"sha256:c01bd450ec790ba37e6647b5ac3d1d76e58a7769aa71524a5445da9400a27a57","observation_id":"78c66bae-9ad0-42bd-91fa-14c5b9123320","resolution":{"observed_at":"2026-08-07T12:38:44.451280Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.07363","last_updated":"2025-04-23T05:11:59Z","snapshot_observed_at":"2026-08-13T06:37:55.969808Z","submitted_at":"2025-04-10T01:09:30Z","title":"Towards Distribution Matching between Collaborative and Language Spaces for Generative Recommendation","version":2},"cited_work":{"arxiv_id":"2504.07363","doi":null,"metadata_source":"pith","pith_arxiv_id":"2504.07363","snapshot_observed_at":"2026-08-07T12:38:43.834437Z","title":"Towards Distribution Matching between Collaborative and Language Spaces for Generative Recommendation","venue":"cs.IR","work_id":"ed304c4c-ef2f-4b6d-8d16-4d6491d736d9","year":2025},"citing_paper":{"arxiv_id":"2505.24172","last_updated":"2025-05-30T03:32:26Z","snapshot_observed_at":"2026-08-12T05:28:32.469188Z","submitted_at":"2025-05-30T03:32:26Z","title":"Heterogeneous Graph Masked Contrastive Learning for Robust Recommendation","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:43.625151Z"},"links":{"cited_paper":"/paper/2504.07363","citing_paper":"/paper/2505.24172"},"observation_digest":"sha256:26ec21277aaeca57632755a53a97a37bec8eb0b332c407d483b49c3345c0bb8b","observation_id":"df95c0e9-f28e-4f2c-beb0-445e2e9e6c17","resolution":{"observed_at":"2026-08-07T12:38:43.955640Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2505.24172","last_updated":"2025-05-30T03:32:26Z","latest_version":1,"primary_category":"cs.IR","snapshot_observed_at":"2026-08-12T05:28:32.469188Z","submitted_at":"2025-05-30T03:32:26Z","title":"Heterogeneous Graph Masked Contrastive Learning for Robust Recommendation"},"reference_resolution":{"displayed":59,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":7,"verified_exact":2,"verified_fuzzy":50},"total_outbound_references":59},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 59 of 59 outbound references and 0 inbound Pith citation observations for arXiv:2505.24172."}