{"as_of":"2026-08-10T03:53:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:11f139ed05d3e7304f27ee165de387e27d0a299559bac212c381db3de5104b15","coverage":[{"denominator":43,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":43,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-22T19:24:48.300461Z","state":"measured"},{"denominator":43,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":43,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+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/2504.13614/citation-record","integrity":"/paper/2504.13614/integrity","json":"/paper/2504.13614/citation-record.json","paper":"/paper/2504.13614"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2406.04267","last_updated":"2024-10-24T23:12:55Z","snapshot_observed_at":"2026-08-10T00:56:38.864790Z","submitted_at":"2024-06-06T17:14:44Z","title":"Transformers need glasses! Information over-squashing in language tasks","version":2},"cited_work":{"arxiv_id":"2406.04267","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.04267","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Transformers need glasses! information over-squashing in language tasks","venue":null,"work_id":"c10053f9-4314-4c9e-97f4-222c384f4fdb","year":2024},"citing_paper":{"arxiv_id":"2504.13614","last_updated":"2026-05-05T15:56:15Z","snapshot_observed_at":"2026-07-06T21:11:24.852957Z","submitted_at":"2025-04-18T10:42:16Z","title":"Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-22T19:24:48.300461Z"},"links":{"cited_paper":"/paper/2406.04267","citing_paper":"/paper/2504.13614"},"observation_digest":"sha256:c0c71bee0baf3fce76bdca088fb2a195a178ab673f6026785a2364aa74e15513","observation_id":"d29b7163-eb24-44aa-99a0-f3e072683d07","resolution":{"observed_at":"2026-05-22T19:25:03.714531Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2411.15671","last_updated":"2024-11-23T23:24:42Z","snapshot_observed_at":"2026-07-06T19:55:59.816003Z","submitted_at":"2024-11-23T23:24:42Z","title":"Best of Both Worlds: Advantages of Hybrid Graph Sequence Models","version":1},"cited_work":{"arxiv_id":"2411.15671","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2411.15671","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Best of both worlds: Advantages of hybrid graph sequence models","venue":null,"work_id":"b74cc976-59de-44f5-8734-600fe0b1c447","year":2024},"citing_paper":{"arxiv_id":"2504.13614","last_updated":"2026-05-05T15:56:15Z","snapshot_observed_at":"2026-07-06T21:11:24.852957Z","submitted_at":"2025-04-18T10:42:16Z","title":"Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-22T19:24:48.300461Z"},"links":{"cited_paper":"/paper/2411.15671","citing_paper":"/paper/2504.13614"},"observation_digest":"sha256:aba4a51b3c5a2362ba7dcc2d59e58101c00f6d9b01409615fa51a352bc93bff6","observation_id":"ca56b91e-3bc1-4e50-a2d6-3a71cbb34ace","resolution":{"observed_at":"2026-05-22T19:25:03.730397Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2311.06047","last_updated":"2023-11-10T13:18:08Z","snapshot_observed_at":"2026-08-06T03:39:22.462099Z","submitted_at":"2023-11-10T13:18:08Z","title":"Fast unfolding of communities in large networks: 15 years later","version":1},"cited_work":{"arxiv_id":"2311.06047","doi":"10.48550/arxiv.2311.06047","metadata_source":"arxiv_reference","pith_arxiv_id":"2311.06047","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Blondel, Jean-Loup Guillaume, and Renaud Lambiotte","venue":"arXiv (Cornell University)","work_id":"6b80bcce-0416-4408-8789-dd79f53a8465","year":2023},"citing_paper":{"arxiv_id":"2504.13614","last_updated":"2026-05-05T15:56:15Z","snapshot_observed_at":"2026-07-06T21:11:24.852957Z","submitted_at":"2025-04-18T10:42:16Z","title":"Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-22T19:24:48.300461Z"},"links":{"cited_paper":"/paper/2311.06047","citing_paper":"/paper/2504.13614"},"observation_digest":"sha256:c475a509ba8326d3c34340464a3edda943cf47870a1c3cbc96329e578164ae6c","observation_id":"037b341f-8641-47ae-be83-dc969bad8328","resolution":{"observed_at":"2026-05-22T19:25:03.723962Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-05T21:23:00.469572Z","title":"Half a decade of graph convolutional networks","venue":null,"work_id":"4786dfaf-03a7-4057-8448-c4990c68ad90","year":2022},"citing_paper":{"arxiv_id":"2504.13614","last_updated":"2026-05-05T15:56:15Z","snapshot_observed_at":"2026-07-06T21:11:24.852957Z","submitted_at":"2025-04-18T10:42:16Z","title":"Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-22T19:24:48.300461Z"},"links":{"citing_paper":"/paper/2504.13614"},"observation_digest":"sha256:d4ded300936fc7e55b6707c6d0ff2da4f804a6995df1a33b7ef1dc9fa6653bfb","observation_id":"98c94683-377a-4903-9c5b-93a99a55268b","resolution":{"observed_at":"2026-05-22T19:25:04.186733Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-05T21:23:00.469572Z","title":"Revisiting graph based collab- orative filtering: A linear residual graph convolutional network approach","venue":null,"work_id":"17128ecf-630b-485f-8dee-bbc046005caa","year":2020},"citing_paper":{"arxiv_id":"2504.13614","last_updated":"2026-05-05T15:56:15Z","snapshot_observed_at":"2026-07-06T21:11:24.852957Z","submitted_at":"2025-04-18T10:42:16Z","title":"Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-22T19:24:48.300461Z"},"links":{"citing_paper":"/paper/2504.13614"},"observation_digest":"sha256:cd6ac3a057f98587bd57c750f3125cecc73208ee6815b38a458d5a5e81da3c95","observation_id":"3e5c7245-0277-49ea-b26a-817e0f5dc70e","resolution":{"observed_at":"2026-05-22T19:25:04.184996Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-05T21:23:00.469572Z","title":"Myers, and Jure Leskovec","venue":null,"work_id":"7bec5ccb-1134-4b23-865d-eab74c8a4e0b","year":2011},"citing_paper":{"arxiv_id":"2504.13614","last_updated":"2026-05-05T15:56:15Z","snapshot_observed_at":"2026-07-06T21:11:24.852957Z","submitted_at":"2025-04-18T10:42:16Z","title":"Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-22T19:24:48.300461Z"},"links":{"citing_paper":"/paper/2504.13614"},"observation_digest":"sha256:85e7a74cb3309a01d5847f78d41db39358867f8dd33222021cb4a9b865cf027f","observation_id":"8bee33ef-61fe-48fd-b113-04ba0aec1569","resolution":{"observed_at":"2026-05-22T19:25:04.203296Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-05T21:23:00.469572Z","title":"Unified denoising training for recommendation","venue":null,"work_id":"36eb8197-a59c-440f-92c9-2f58088f57ca","year":2024},"citing_paper":{"arxiv_id":"2504.13614","last_updated":"2026-05-05T15:56:15Z","snapshot_observed_at":"2026-07-06T21:11:24.852957Z","submitted_at":"2025-04-18T10:42:16Z","title":"Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-22T19:24:48.300461Z"},"links":{"citing_paper":"/paper/2504.13614"},"observation_digest":"sha256:059d48c5b3d9923770627f1019eb64aef15d6c4a19b4cf45fdecc21e0198bc6d","observation_id":"9e04caa4-63a8-4f7a-9865-8250146e59de","resolution":{"observed_at":"2026-05-22T19:25:04.199145Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"67844022-ebf2-4225-bbf3-1c8113e3a838","year":2023},"citing_paper":{"arxiv_id":"2504.13614","last_updated":"2026-05-05T15:56:15Z","snapshot_observed_at":"2026-07-06T21:11:24.852957Z","submitted_at":"2025-04-18T10:42:16Z","title":"Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-22T19:24:48.300461Z"},"links":{"citing_paper":"/paper/2504.13614"},"observation_digest":"sha256:7a810f90afab8f42f50f49d2c37022d3e4d181d48a80bc3040e813fd7567e510","observation_id":"3c471288-f8ee-4f72-a390-b5afad5e3711","resolution":{"observed_at":"2026-05-22T19:25:04.192444Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-05T21:23:00.469572Z","title":"A survey of graph neural networks for recom- mender systems: Challenges, methods, and directions","venue":null,"work_id":"b3b478c3-d65e-4da4-9234-424fbf5c1bff","year":2023},"citing_paper":{"arxiv_id":"2504.13614","last_updated":"2026-05-05T15:56:15Z","snapshot_observed_at":"2026-07-06T21:11:24.852957Z","submitted_at":"2025-04-18T10:42:16Z","title":"Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-22T19:24:48.300461Z"},"links":{"citing_paper":"/paper/2504.13614"},"observation_digest":"sha256:8574f5335d869ba4481bbb874352ad1082145c4f02925f4ab78044ae43455ced","observation_id":"5aeaca14-adfc-4916-b842-11e007e74337","resolution":{"observed_at":"2026-05-22T19:25:04.172680Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-05T21:23:00.469572Z","title":"Content augmented graph neural networks","venue":null,"work_id":"7d3f0626-5b6e-4b05-90c9-adc4f784f006","year":2024},"citing_paper":{"arxiv_id":"2504.13614","last_updated":"2026-05-05T15:56:15Z","snapshot_observed_at":"2026-07-06T21:11:24.852957Z","submitted_at":"2025-04-18T10:42:16Z","title":"Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-22T19:24:48.300461Z"},"links":{"citing_paper":"/paper/2504.13614"},"observation_digest":"sha256:50cabbe10dc83411a00977d4ec6e7d9b662537a6c46f53f3431b4c278835afa7","observation_id":"fbc56692-10fc-4f61-a243-9dcca8de5d03","resolution":{"observed_at":"2026-05-22T19:25:04.196922Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.03365","last_updated":"2025-08-05T10:31:51Z","snapshot_observed_at":"2026-08-08T16:16:03.427402Z","submitted_at":"2024-01-31T11:03:58Z","title":"Heterophily-Aware Fair Recommendation using Graph Convolutional Networks","version":4},"cited_work":{"arxiv_id":"2402.03365","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.03365","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Heterophily-aware fair recommendation using graph convolu- tional networks","venue":null,"work_id":"207d6ef2-1fd2-4f51-b063-b21c32d462aa","year":2024},"citing_paper":{"arxiv_id":"2504.13614","last_updated":"2026-05-05T15:56:15Z","snapshot_observed_at":"2026-07-06T21:11:24.852957Z","submitted_at":"2025-04-18T10:42:16Z","title":"Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-22T19:24:48.300461Z"},"links":{"cited_paper":"/paper/2402.03365","citing_paper":"/paper/2504.13614"},"observation_digest":"sha256:79daba9bc8f4be3d776c8ea055addd310c0dcb48ffe767958217775ca39df7d7","observation_id":"64d7e8b0-7646-4ed6-bc8d-6bb296e0e6fd","resolution":{"observed_at":"2026-05-22T19:25:03.720322Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-05T21:23:00.469572Z","title":"Disentangling popularity and quality: An edge classification approach for fair recommendation","venue":null,"work_id":"5b5400cc-d3d8-47cb-8da2-fc4514058f06","year":2025},"citing_paper":{"arxiv_id":"2504.13614","last_updated":"2026-05-05T15:56:15Z","snapshot_observed_at":"2026-07-06T21:11:24.852957Z","submitted_at":"2025-04-18T10:42:16Z","title":"Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-22T19:24:48.300461Z"},"links":{"citing_paper":"/paper/2504.13614"},"observation_digest":"sha256:c9f81113fba12546c476819771b99bc5ced4ee1daa4e1d25306b289edf43afe1","observation_id":"8743cf51-9c1d-4f1b-afe8-324bea068da5","resolution":{"observed_at":"2026-05-22T19:25:04.194704Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-05T21:23:00.469572Z","title":"Maxwell Harper and Joseph A","venue":null,"work_id":"b3ae7043-1f7e-4569-87e8-39fb17568d1c","year":2016},"citing_paper":{"arxiv_id":"2504.13614","last_updated":"2026-05-05T15:56:15Z","snapshot_observed_at":"2026-07-06T21:11:24.852957Z","submitted_at":"2025-04-18T10:42:16Z","title":"Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-22T19:24:48.300461Z"},"links":{"citing_paper":"/paper/2504.13614"},"observation_digest":"sha256:c462834bf0da02d662f2c137d6fe18fe8eb7eb1e9372ed5cc3f7f7cd1fac99d9","observation_id":"2be43bb8-94c4-461e-be11-315a8aeb86b3","resolution":{"observed_at":"2026-05-22T19:25:04.190401Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"b10288a9-e6ac-4ff4-b701-4501d46027c7","year":2016},"citing_paper":{"arxiv_id":"2504.13614","last_updated":"2026-05-05T15:56:15Z","snapshot_observed_at":"2026-07-06T21:11:24.852957Z","submitted_at":"2025-04-18T10:42:16Z","title":"Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-22T19:24:48.300461Z"},"links":{"citing_paper":"/paper/2504.13614"},"observation_digest":"sha256:74e892ee8c00511d2aaad5879028525eaf8fc399002702e916c4a53e3bc14530","observation_id":"dc769f10-836b-40ea-9006-b02ec8838cdb","resolution":{"observed_at":"2026-05-22T19:25:04.188343Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-05T21:23:00.469572Z","title":"Lightgcn: Simplifying and powering graph convolution network for recommendation","venue":null,"work_id":"dfe7f0fa-c6ca-404e-acce-a01afcd364f7","year":2020},"citing_paper":{"arxiv_id":"2504.13614","last_updated":"2026-05-05T15:56:15Z","snapshot_observed_at":"2026-07-06T21:11:24.852957Z","submitted_at":"2025-04-18T10:42:16Z","title":"Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-22T19:24:48.300461Z"},"links":{"citing_paper":"/paper/2504.13614"},"observation_digest":"sha256:0a6566e629c22342f33164d70457a2f94a9cbb5a60583c2c3ae8640f71d9a2ba","observation_id":"59c89268-d660-4f81-94da-75814129e129","resolution":{"observed_at":"2026-05-22T19:25:04.187056Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-05T21:23:00.469572Z","title":"Neural collaborative filtering","venue":null,"work_id":"05ae372b-0ced-4306-ab47-8f47a2a34198","year":2017},"citing_paper":{"arxiv_id":"2504.13614","last_updated":"2026-05-05T15:56:15Z","snapshot_observed_at":"2026-07-06T21:11:24.852957Z","submitted_at":"2025-04-18T10:42:16Z","title":"Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-22T19:24:48.300461Z"},"links":{"citing_paper":"/paper/2504.13614"},"observation_digest":"sha256:555912608e5c6579d32f6bd9e0c76d15a11f672b19073bdcbc0265251f283d56","observation_id":"8b2b3d2b-59cd-4483-b2ce-e2d3e1e35520","resolution":{"observed_at":"2026-05-22T19:25:04.207471Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-05T21:23:00.469572Z","title":"Session-based recommendations with recurrent neural networks","venue":null,"work_id":"80a592ef-5557-4b1b-bb1e-2cb6fc2aef11","year":2016},"citing_paper":{"arxiv_id":"2504.13614","last_updated":"2026-05-05T15:56:15Z","snapshot_observed_at":"2026-07-06T21:11:24.852957Z","submitted_at":"2025-04-18T10:42:16Z","title":"Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-22T19:24:48.300461Z"},"links":{"citing_paper":"/paper/2504.13614"},"observation_digest":"sha256:f2230412c7acbfd7f236d200715a00cd394f9fd123b642c77b0cb6a331f957bb","observation_id":"193c7cc6-b8e2-49de-8395-9c0af21d6105","resolution":{"observed_at":"2026-05-22T19:25:04.185270Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"58e3b3c1-115f-4a43-94a9-f38ca4647d3e","year":2018},"citing_paper":{"arxiv_id":"2504.13614","last_updated":"2026-05-05T15:56:15Z","snapshot_observed_at":"2026-07-06T21:11:24.852957Z","submitted_at":"2025-04-18T10:42:16Z","title":"Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-22T19:24:48.300461Z"},"links":{"citing_paper":"/paper/2504.13614"},"observation_digest":"sha256:3dcd46639a3f54e6ba55ae2578df6e9c49e6b2ad00c4bd2bb9711e08a741b2ae","observation_id":"5a47c67b-5759-42fc-a133-b50e81e9c0de","resolution":{"observed_at":"2026-05-22T19:25:04.183414Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-05T21:23:00.469572Z","title":"Kipf and Max Welling","venue":null,"work_id":"ab1ffaaf-271f-4ec5-97d4-95768f16635f","year":2017},"citing_paper":{"arxiv_id":"2504.13614","last_updated":"2026-05-05T15:56:15Z","snapshot_observed_at":"2026-07-06T21:11:24.852957Z","submitted_at":"2025-04-18T10:42:16Z","title":"Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-22T19:24:48.300461Z"},"links":{"citing_paper":"/paper/2504.13614"},"observation_digest":"sha256:be79c4a1c7272fce597291db69ec63586c1fbaa776b2641ab8867cc6c212c276","observation_id":"f65ed63f-c24a-43ce-a11f-8fd14629c82d","resolution":{"observed_at":"2026-05-22T19:25:04.181927Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-05T21:23:00.469572Z","title":"Bell, and Chris Volinsky","venue":null,"work_id":"5ad02608-d0f0-4737-9c74-8b15940255b6","year":2009},"citing_paper":{"arxiv_id":"2504.13614","last_updated":"2026-05-05T15:56:15Z","snapshot_observed_at":"2026-07-06T21:11:24.852957Z","submitted_at":"2025-04-18T10:42:16Z","title":"Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-22T19:24:48.300461Z"},"links":{"citing_paper":"/paper/2504.13614"},"observation_digest":"sha256:22fd042de5b37cbae85398517dc68f3e80786dcc8de76de8ce77622fce33cade","observation_id":"55887ddf-8fdc-4a07-8e65-be6bac09fcc9","resolution":{"observed_at":"2026-05-22T19:25:04.158148Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"1b61d1b1-734e-4dd7-bb31-4c639fc6dd3f","year":2020},"citing_paper":{"arxiv_id":"2504.13614","last_updated":"2026-05-05T15:56:15Z","snapshot_observed_at":"2026-07-06T21:11:24.852957Z","submitted_at":"2025-04-18T10:42:16Z","title":"Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-22T19:24:48.300461Z"},"links":{"citing_paper":"/paper/2504.13614"},"observation_digest":"sha256:d2c1465822109c55adea3144f70ea9936cf7c5ada1955169077632f49edb2ddd","observation_id":"6b5cc43e-7741-4dce-a3c2-6cd0f213ec96","resolution":{"observed_at":"2026-05-22T19:25:04.180381Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-05T21:23:00.469572Z","title":"TEA: A sequential recommendation framework via temporally evolving aggregations","venue":null,"work_id":"3c4359b4-45c6-491b-b96c-31dd18dd46c4","year":2024},"citing_paper":{"arxiv_id":"2504.13614","last_updated":"2026-05-05T15:56:15Z","snapshot_observed_at":"2026-07-06T21:11:24.852957Z","submitted_at":"2025-04-18T10:42:16Z","title":"Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-22T19:24:48.300461Z"},"links":{"citing_paper":"/paper/2504.13614"},"observation_digest":"sha256:d6a11d96ca5c7d550ae5d5347370c9ca820668ee69e4a7f46ded452a96ec62cc","observation_id":"dfccc6d5-df66-4b86-874b-200d3c140b40","resolution":{"observed_at":"2026-05-22T19:25:04.190616Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-05T21:23:00.469572Z","title":"Heterogeneous multidomain recommender system through adversarial learning","venue":null,"work_id":"13204dbd-678d-453a-9a75-aaf650919339","year":2023},"citing_paper":{"arxiv_id":"2504.13614","last_updated":"2026-05-05T15:56:15Z","snapshot_observed_at":"2026-07-06T21:11:24.852957Z","submitted_at":"2025-04-18T10:42:16Z","title":"Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-22T19:24:48.300461Z"},"links":{"citing_paper":"/paper/2504.13614"},"observation_digest":"sha256:d6e5f0a35f61c4166e44d4890741c3c9ab06b1b1e6535aa37ffaf7f4a58792d9","observation_id":"389938b1-cfb7-45c7-8a4f-2e87e7086ab2","resolution":{"observed_at":"2026-05-22T19:25:04.176647Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-05T21:23:00.469572Z","title":"K-plet recurrent neural networks for sequential recommendation","venue":null,"work_id":"258eb206-c4ba-40aa-a8c1-7100572535eb","year":2018},"citing_paper":{"arxiv_id":"2504.13614","last_updated":"2026-05-05T15:56:15Z","snapshot_observed_at":"2026-07-06T21:11:24.852957Z","submitted_at":"2025-04-18T10:42:16Z","title":"Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-22T19:24:48.300461Z"},"links":{"citing_paper":"/paper/2504.13614"},"observation_digest":"sha256:ff34f17110aa7ae74116757a5a1166a5d3fb0eb074f135e01466806b4f1ada6f","observation_id":"9dcc0c1d-74de-4fd1-99be-e12b9c370989","resolution":{"observed_at":"2026-05-22T19:25:04.148439Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-05T21:23:00.469572Z","title":"Selfgnn: Self-supervised graph neural networks for sequential recommendation","venue":null,"work_id":"eaf0f943-7819-4899-8ef8-58b54633cc71","year":2024},"citing_paper":{"arxiv_id":"2504.13614","last_updated":"2026-05-05T15:56:15Z","snapshot_observed_at":"2026-07-06T21:11:24.852957Z","submitted_at":"2025-04-18T10:42:16Z","title":"Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-22T19:24:48.300461Z"},"links":{"citing_paper":"/paper/2504.13614"},"observation_digest":"sha256:be604c5279e5a72c422d0345b5ef55dfee79e894bb7f15743653d13a2b14e9c5","observation_id":"b7d99a87-0469-4e38-9a94-6869aed92819","resolution":{"observed_at":"2026-05-22T19:25:04.174853Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-05T21:23:00.469572Z","title":"Ultragcn: Ultra simplification of graph convolutional networks for recommendation","venue":null,"work_id":"47d6e411-7cd4-4e35-ba50-79fb71fbd445","year":2021},"citing_paper":{"arxiv_id":"2504.13614","last_updated":"2026-05-05T15:56:15Z","snapshot_observed_at":"2026-07-06T21:11:24.852957Z","submitted_at":"2025-04-18T10:42:16Z","title":"Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-05-22T19:24:48.300461Z"},"links":{"citing_paper":"/paper/2504.13614"},"observation_digest":"sha256:248b94c5c474806fb6d92b83dbb67874aec2884dc44f8a56777a37e666ad3c38","observation_id":"9d692231-7a07-4d9e-a02c-92ee5f248c0c","resolution":{"observed_at":"2026-05-22T19:25:04.209819Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-05T21:23:00.469572Z","title":"Factorizing personalized markov chains for next-basket recommendation","venue":null,"work_id":"f433f357-47a2-4291-a8fe-76094610e316","year":2010},"citing_paper":{"arxiv_id":"2504.13614","last_updated":"2026-05-05T15:56:15Z","snapshot_observed_at":"2026-07-06T21:11:24.852957Z","submitted_at":"2025-04-18T10:42:16Z","title":"Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-22T19:24:48.300461Z"},"links":{"citing_paper":"/paper/2504.13614"},"observation_digest":"sha256:8fec45287ac3f538f1512b6c29178271093993abb9da5b839aba18e4eec9bf42","observation_id":"962c5184-6caf-4748-897e-d72723428f46","resolution":{"observed_at":"2026-05-22T19:25:04.172851Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-05T21:23:00.469572Z","title":"Unbi- ased recommender learning from missing-not-at-random implicit feedback","venue":null,"work_id":"36ab268e-7006-437c-8f11-7eabce5ff46d","year":2020},"citing_paper":{"arxiv_id":"2504.13614","last_updated":"2026-05-05T15:56:15Z","snapshot_observed_at":"2026-07-06T21:11:24.852957Z","submitted_at":"2025-04-18T10:42:16Z","title":"Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-05-22T19:24:48.300461Z"},"links":{"citing_paper":"/paper/2504.13614"},"observation_digest":"sha256:242d722a14a47863507ef898941b0e53862a87a905ee2c5c65a2a4c2688dda2b","observation_id":"c2aa73d4-e82e-4936-bb84-39764905ee0b","resolution":{"observed_at":"2026-05-22T19:25:04.211717Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-05T21:23:00.469572Z","title":"Bert4rec: Sequential recommendation with bidirectional encoder representations from transformer","venue":null,"work_id":"dc68d64a-1136-4f1c-91fb-a04c636bcd6f","year":2019},"citing_paper":{"arxiv_id":"2504.13614","last_updated":"2026-05-05T15:56:15Z","snapshot_observed_at":"2026-07-06T21:11:24.852957Z","submitted_at":"2025-04-18T10:42:16Z","title":"Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-22T19:24:48.300461Z"},"links":{"citing_paper":"/paper/2504.13614"},"observation_digest":"sha256:b84e989dc1f1c2a55ffd305629231925e55edc756d4194391d0e69c70a84297f","observation_id":"da55d259-8009-4872-a8ff-917ed878d6b5","resolution":{"observed_at":"2026-05-22T19:25:04.171008Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"19acb3e9-c9b3-489c-9a82-89c8e540b054","year":2022},"citing_paper":{"arxiv_id":"2504.13614","last_updated":"2026-05-05T15:56:15Z","snapshot_observed_at":"2026-07-06T21:11:24.852957Z","submitted_at":"2025-04-18T10:42:16Z","title":"Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-05-22T19:24:48.300461Z"},"links":{"citing_paper":"/paper/2504.13614"},"observation_digest":"sha256:977c7dbd70f6b88f7d195475c263b4cae21e9e5fbdb872e5c699538599a47188","observation_id":"bc94f551-6cb7-4f1e-893e-15c55819a44c","resolution":{"observed_at":"2026-05-22T19:25:04.181684Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1706.02263","last_updated":"2017-10-25T19:20:03Z","snapshot_observed_at":"2026-08-06T09:26:13.510783Z","submitted_at":"2017-06-07T17:05:19Z","title":"Graph Convolutional Matrix Completion","version":2},"cited_work":{"arxiv_id":"1706.02263","doi":null,"metadata_source":"pith","pith_arxiv_id":"1706.02263","snapshot_observed_at":"2026-07-04T05:49:38.606193Z","title":"Graph Convolutional Matrix Completion","venue":"stat.ML","work_id":"9c7b0546-9ab6-45b1-907a-8eb0288f5a12","year":2017},"citing_paper":{"arxiv_id":"2504.13614","last_updated":"2026-05-05T15:56:15Z","snapshot_observed_at":"2026-07-06T21:11:24.852957Z","submitted_at":"2025-04-18T10:42:16Z","title":"Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-05-22T19:24:48.300461Z"},"links":{"cited_paper":"/paper/1706.02263","citing_paper":"/paper/2504.13614"},"observation_digest":"sha256:7e3d40c2824f64f3f9ea3c29f402a696f9b07830aaef5212f7e918e36664e1e6","observation_id":"2b999b86-2154-4ac4-9987-a84452c71fbc","resolution":{"observed_at":"2026-05-22T19:25:03.727119Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2408.08208","last_updated":"2025-06-07T04:28:59Z","snapshot_observed_at":"2026-07-06T19:01:10.268051Z","submitted_at":"2024-08-15T15:18:46Z","title":"LLM4DSR: Leveraging Large Language Model for Denoising Sequential Recommendation","version":3},"cited_work":{"arxiv_id":"2408.08208","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2408.08208","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"LLM4DSR: leveraing large language model for denoising sequential recommen- dation","venue":null,"work_id":"16b2b0cc-2590-463b-a266-03d80eeae221","year":2024},"citing_paper":{"arxiv_id":"2504.13614","last_updated":"2026-05-05T15:56:15Z","snapshot_observed_at":"2026-07-06T21:11:24.852957Z","submitted_at":"2025-04-18T10:42:16Z","title":"Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-05-22T19:24:48.300461Z"},"links":{"cited_paper":"/paper/2408.08208","citing_paper":"/paper/2504.13614"},"observation_digest":"sha256:21d24ddab921d6b01e66fb42f5abdbb165fa280c799cfe35745c5fba7e489bdb","observation_id":"470d9c06-2f88-4d97-8b76-f02005d52df8","resolution":{"observed_at":"2026-05-22T19:25:03.724524Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-05T21:23:00.469572Z","title":"Neural graph collabora- tive filtering","venue":null,"work_id":"0f82fd94-6221-45f4-8b30-6317080c35d8","year":2019},"citing_paper":{"arxiv_id":"2504.13614","last_updated":"2026-05-05T15:56:15Z","snapshot_observed_at":"2026-07-06T21:11:24.852957Z","submitted_at":"2025-04-18T10:42:16Z","title":"Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-05-22T19:24:48.300461Z"},"links":{"citing_paper":"/paper/2504.13614"},"observation_digest":"sha256:b6bf60fd0268fda7ad1651862173d9711dbab2ee0e25d010d463f217d4901a42","observation_id":"8dbb45fb-4da3-41b4-8980-3b037963051b","resolution":{"observed_at":"2026-05-22T19:25:04.166617Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-05T21:23:00.469572Z","title":"A survey on accuracy-oriented neural recommendation: From collaborative filtering to information-rich recommendation","venue":null,"work_id":"6a00317a-87b4-4f0e-9b48-874a8f622257","year":2023},"citing_paper":{"arxiv_id":"2504.13614","last_updated":"2026-05-05T15:56:15Z","snapshot_observed_at":"2026-07-06T21:11:24.852957Z","submitted_at":"2025-04-18T10:42:16Z","title":"Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-05-22T19:24:48.300461Z"},"links":{"citing_paper":"/paper/2504.13614"},"observation_digest":"sha256:b51ebe7252a5494e18e88157498c6e68a9a6c798f49290075c9ae2629d0a09c7","observation_id":"2073dfca-3683-4919-acab-2c31484561d2","resolution":{"observed_at":"2026-05-22T19:25:04.161242Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-05T21:23:00.469572Z","title":"Session-based recommendation with graph neural networks","venue":null,"work_id":"43b59479-de13-485d-af5d-46b8c883a4ef","year":2019},"citing_paper":{"arxiv_id":"2504.13614","last_updated":"2026-05-05T15:56:15Z","snapshot_observed_at":"2026-07-06T21:11:24.852957Z","submitted_at":"2025-04-18T10:42:16Z","title":"Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-05-22T19:24:48.300461Z"},"links":{"citing_paper":"/paper/2504.13614"},"observation_digest":"sha256:140c8bbd6824759ae43279df674817697277f9ca1f99325933d73457b47ca1db","observation_id":"2586ed81-e9c5-46ae-b8d8-107ff5460ece","resolution":{"observed_at":"2026-05-22T19:25:04.159278Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-05T21:23:00.469572Z","title":"Enhancing robustness in implicit feedback recom- mender systems with subgraph contrastive learning","venue":null,"work_id":"fd2b7def-a613-4c95-8bb1-873a3322442a","year":2025},"citing_paper":{"arxiv_id":"2504.13614","last_updated":"2026-05-05T15:56:15Z","snapshot_observed_at":"2026-07-06T21:11:24.852957Z","submitted_at":"2025-04-18T10:42:16Z","title":"Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-05-22T19:24:48.300461Z"},"links":{"citing_paper":"/paper/2504.13614"},"observation_digest":"sha256:1ff4e22f066af814075bd4a94d7f81bba9468c803cac55053a8f928e86e31ecf","observation_id":"fc6e2921-4ff9-48a1-9a14-4e2a3b397642","resolution":{"observed_at":"2026-05-22T19:25:04.201287Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-05T21:23:00.469572Z","title":"Re- drec: Relation and dynamic aware graph convolutional network for sequential recommendation","venue":null,"work_id":"97605aee-caa3-44ff-bd3a-ce66a1bd97f6","year":2023},"citing_paper":{"arxiv_id":"2504.13614","last_updated":"2026-05-05T15:56:15Z","snapshot_observed_at":"2026-07-06T21:11:24.852957Z","submitted_at":"2025-04-18T10:42:16Z","title":"Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-05-22T19:24:48.300461Z"},"links":{"citing_paper":"/paper/2504.13614"},"observation_digest":"sha256:35f616aba48c40cdacdd37a83ce83acbacc1e43dd13789734348ee226c388fd8","observation_id":"693b5eab-5398-418a-82ff-d5a6e40776aa","resolution":{"observed_at":"2026-05-22T19:25:04.157476Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-05T21:23:00.469572Z","title":"Denoised graph collaborative filtering via neighborhood similarity and dynamic thresholding","venue":null,"work_id":"d7de2bde-c87c-4108-ad2b-38366e0c7865","year":2024},"citing_paper":{"arxiv_id":"2504.13614","last_updated":"2026-05-05T15:56:15Z","snapshot_observed_at":"2026-07-06T21:11:24.852957Z","submitted_at":"2025-04-18T10:42:16Z","title":"Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-05-22T19:24:48.300461Z"},"links":{"citing_paper":"/paper/2504.13614"},"observation_digest":"sha256:5b06c5217a0b4ef2e6a520d7ea0eefc654df914dc1bef7bf58fea24761bb7e26","observation_id":"c3e12c13-c107-41fd-b35b-e9cca5e58802","resolution":{"observed_at":"2026-05-22T19:25:04.155598Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-05T21:23:00.469572Z","title":"Dynamic graph neural networks for sequential recommendation","venue":null,"work_id":"09f8b9db-e3f6-4167-b63b-f574c56ce4ea","year":2023},"citing_paper":{"arxiv_id":"2504.13614","last_updated":"2026-05-05T15:56:15Z","snapshot_observed_at":"2026-07-06T21:11:24.852957Z","submitted_at":"2025-04-18T10:42:16Z","title":"Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-05-22T19:24:48.300461Z"},"links":{"citing_paper":"/paper/2504.13614"},"observation_digest":"sha256:2b1570748aa0aa1f5e649c5a58e6d702dba28b62ec5d35462f37dd99b209eb91","observation_id":"3a5c915c-a132-43d4-9291-361e900aaa9f","resolution":{"observed_at":"2026-05-22T19:25:04.153664Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-05T21:23:00.469572Z","title":"Dis- entangling long and short-term interests for recommendation","venue":null,"work_id":"6af6ce51-83a2-417f-8b79-6d3dc23e3996","year":2022},"citing_paper":{"arxiv_id":"2504.13614","last_updated":"2026-05-05T15:56:15Z","snapshot_observed_at":"2026-07-06T21:11:24.852957Z","submitted_at":"2025-04-18T10:42:16Z","title":"Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-05-22T19:24:48.300461Z"},"links":{"citing_paper":"/paper/2504.13614"},"observation_digest":"sha256:2720c4b238b973edf995814fc3452e782dc5955d71d8c9c0b55accdaa56faf8f","observation_id":"ed8190ef-8829-4342-bbe0-f37235024757","resolution":{"observed_at":"2026-05-22T19:25:04.205646Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-05T21:23:00.469572Z","title":"Incorporating price into recom- mendation with graph convolutional networks","venue":null,"work_id":"a04fa2e5-a929-4049-87c5-e968c1d00998","year":2023},"citing_paper":{"arxiv_id":"2504.13614","last_updated":"2026-05-05T15:56:15Z","snapshot_observed_at":"2026-07-06T21:11:24.852957Z","submitted_at":"2025-04-18T10:42:16Z","title":"Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-05-22T19:24:48.300461Z"},"links":{"citing_paper":"/paper/2504.13614"},"observation_digest":"sha256:1a5ead290bbc5bd4e155bfd4eabf77711392122d6c15b8102726d933a5ef8922","observation_id":"f8f3f30f-f2b5-4f84-9bbd-aab287556c57","resolution":{"observed_at":"2026-05-22T19:25:04.151609Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-05T21:23:00.469572Z","title":"Graph neural networks: A review of methods and applica- tions","venue":null,"work_id":"3159abfa-5321-442f-a69c-3c76bab60165","year":2020},"citing_paper":{"arxiv_id":"2504.13614","last_updated":"2026-05-05T15:56:15Z","snapshot_observed_at":"2026-07-06T21:11:24.852957Z","submitted_at":"2025-04-18T10:42:16Z","title":"Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-05-22T19:24:48.300461Z"},"links":{"citing_paper":"/paper/2504.13614"},"observation_digest":"sha256:4f714852933e093b01136b41a1a27815d7dcc5561c9c50d138373e00bed3a2ff","observation_id":"5ba7bcb3-b93b-418e-b859-718d3ee8f4ef","resolution":{"observed_at":"2026-05-22T19:25:04.163047Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-05T21:23:00.469572Z","title":"Centrality- based and similarity-based neighborhood extension in graph neural networks","venue":null,"work_id":"117bc897-beb0-487f-904b-8bd76c9aa745","year":2024},"citing_paper":{"arxiv_id":"2504.13614","last_updated":"2026-05-05T15:56:15Z","snapshot_observed_at":"2026-07-06T21:11:24.852957Z","submitted_at":"2025-04-18T10:42:16Z","title":"Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-05-22T19:24:48.300461Z"},"links":{"citing_paper":"/paper/2504.13614"},"observation_digest":"sha256:33020935f8650c9cc9a9c9a1c545d6b92556b5a51def7f95ad4f4a4878bcf536","observation_id":"db3463fc-1c9d-4713-9f70-ea397d044c9c","resolution":{"observed_at":"2026-05-22T19:25:04.164767Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2504.13614","last_updated":"2026-05-05T15:56:15Z","latest_version":2,"primary_category":"cs.IR","snapshot_observed_at":"2026-07-06T21:11:24.852957Z","submitted_at":"2025-04-18T10:42:16Z","title":"Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation"},"reference_resolution":{"displayed":43,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":5,"verified_exact":6,"verified_fuzzy":32},"total_outbound_references":43},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2504.13614."}