{"as_of":"2026-08-14T08:57:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:2f58f3343450fe5ef9ba4ae75955de930c813fb22567283e092cb0e700dc7997","coverage":[{"denominator":54,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":54,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T14:18:45.726648Z","state":"measured"},{"denominator":55,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":55,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-18T03:47:08.208082Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-18T03:50:52.056899Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2505.19473","last_updated":"2025-05-26T03:52:41Z","snapshot_observed_at":"2026-08-09T16:06:33.300857Z","submitted_at":"2025-05-26T03:52:41Z","title":"Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs","version":1},"cited_work":{"arxiv_id":"2505.19473","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2505.19473","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Im- proving recommendation fairness without sensitive attributes using multi-persona llms","venue":null,"work_id":"4676caa4-9103-4f97-84c2-ba1df6feccc9","year":null},"citing_paper":{"arxiv_id":"2510.27157","last_updated":"2026-05-09T03:19:23Z","snapshot_observed_at":"2026-08-11T00:20:03.132239Z","submitted_at":"2025-10-31T04:02:58Z","title":"A Survey on Generative Recommendation: Data, Model, and Tasks","version":2},"reference_index":200,"source":"pdf_text","source_observed_at":"2026-05-18T03:47:08.208082Z"},"links":{"cited_paper":"/paper/2505.19473","citing_paper":"/paper/2510.27157"},"observation_digest":"sha256:b5c605a9c373698fb809e85ef43aa01564702740e28442d9b341f44020c15c54","observation_id":"1bed4c9c-d150-4196-b6d8-85a1aa162d77","resolution":{"observed_at":"2026-05-18T03:50:52.059712Z","resolver_source":"arxiv_id","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"}}],"links":{"evidence":"/evidence","html":"/paper/2505.19473/citation-record","integrity":"/paper/2505.19473/integrity","json":"/paper/2505.19473/citation-record.json","paper":"/paper/2505.19473"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:18:58.193505Z","title":"Learning optimal and fair decision trees for non-discriminative decision-making","venue":null,"work_id":"54058334-a09c-468d-bc76-e77f2226670a","year":2019},"citing_paper":{"arxiv_id":"2505.19473","last_updated":"2025-05-26T03:52:41Z","snapshot_observed_at":"2026-08-09T16:06:33.300857Z","submitted_at":"2025-05-26T03:52:41Z","title":"Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T14:18:38.653671Z"},"links":{"citing_paper":"/paper/2505.19473"},"observation_digest":"sha256:d7193c32616f62118eca07983d3c5e1a92636c3f2f72d6920af3f0f68a11610a","observation_id":"8e2e7850-8123-4a77-acc8-efe907d507fe","resolution":{"observed_at":"2026-08-07T14:18:58.343410Z","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":"2026-08-07T14:18:57.926969Z","title":"Fairness without demographic data: A survey of approaches","venue":null,"work_id":"0e764214-76b3-4f1b-97ef-53a2c3e5b042","year":2023},"citing_paper":{"arxiv_id":"2505.19473","last_updated":"2025-05-26T03:52:41Z","snapshot_observed_at":"2026-08-09T16:06:33.300857Z","submitted_at":"2025-05-26T03:52:41Z","title":"Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T14:18:38.717428Z"},"links":{"citing_paper":"/paper/2505.19473"},"observation_digest":"sha256:e221b7eeb1fc7769d7ee8fbdd86cd9210041dad182365553aa19761bc1c29518","observation_id":"c40a5a91-df73-4bbe-a581-7b03850cdde9","resolution":{"observed_at":"2026-08-07T14:18:58.043930Z","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":"2026-08-07T14:18:38.862871Z","title":"Tallrec: An effective and efficient tuning framework to align large language model with recommendation","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.19473","last_updated":"2025-05-26T03:52:41Z","snapshot_observed_at":"2026-08-09T16:06:33.300857Z","submitted_at":"2025-05-26T03:52:41Z","title":"Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T14:18:38.862871Z"},"links":{"citing_paper":"/paper/2505.19473"},"observation_digest":"sha256:04b6343c3c4d902ddbe491eeea56dee4bdb5930651ac7ff9c87d118a7d68398b","observation_id":"03b26887-53ee-4ea9-b810-b186b1885715","resolution":{"observed_at":"2026-08-07T14:18:38.862871Z","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":"2026-08-07T14:18:57.634192Z","title":"Compositional fairness constraints for graph embeddings","venue":null,"work_id":"9291b4ae-407a-4f02-afa2-d8f6249147ec","year":2019},"citing_paper":{"arxiv_id":"2505.19473","last_updated":"2025-05-26T03:52:41Z","snapshot_observed_at":"2026-08-09T16:06:33.300857Z","submitted_at":"2025-05-26T03:52:41Z","title":"Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T14:18:38.985479Z"},"links":{"citing_paper":"/paper/2505.19473"},"observation_digest":"sha256:61b01f822f13937a1d077742ed962ccee114f811712d589bb49499a7f1f028ff","observation_id":"0a062610-63ff-437f-ab71-cd0b89ea099b","resolution":{"observed_at":"2026-08-07T14:18:57.760735Z","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":"2026-08-07T14:18:57.325854Z","title":"Universitat Pompeu Fabra, 2009","venue":null,"work_id":"0902bb04-bf28-4646-963e-ac6b4563469b","year":2009},"citing_paper":{"arxiv_id":"2505.19473","last_updated":"2025-05-26T03:52:41Z","snapshot_observed_at":"2026-08-09T16:06:33.300857Z","submitted_at":"2025-05-26T03:52:41Z","title":"Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T14:18:39.139551Z"},"links":{"citing_paper":"/paper/2505.19473"},"observation_digest":"sha256:791677412ad3eac5c2de993ea321126986cc3643776ee51adad15c560a167fc2","observation_id":"2c49e1f5-3189-4b9d-bb23-2ec12036a698","resolution":{"observed_at":"2026-08-07T14:18:57.446030Z","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":"2026-08-07T14:18:57.063428Z","title":"Fairness without demographics through knowledge distillation.Advances in Neural Information Processing Systems, 35:19152–19164, 2022","venue":null,"work_id":"11a22676-eaf6-4867-9151-7a61634ab164","year":2022},"citing_paper":{"arxiv_id":"2505.19473","last_updated":"2025-05-26T03:52:41Z","snapshot_observed_at":"2026-08-09T16:06:33.300857Z","submitted_at":"2025-05-26T03:52:41Z","title":"Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T14:18:39.262716Z"},"links":{"citing_paper":"/paper/2505.19473"},"observation_digest":"sha256:30d011da5a9228d9f955c9f60b15ac4c15af088403d6ae965e2c0c2d34c64154","observation_id":"19aa0f5f-f6fd-4b9b-af82-4d0744573907","resolution":{"observed_at":"2026-08-07T14:18:57.179095Z","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":"2026-08-07T14:18:56.770038Z","title":"Improving recommendation fairness via data augmentation","venue":null,"work_id":"8361e3f1-83af-4113-ac42-1259a6a5dfc6","year":2023},"citing_paper":{"arxiv_id":"2505.19473","last_updated":"2025-05-26T03:52:41Z","snapshot_observed_at":"2026-08-09T16:06:33.300857Z","submitted_at":"2025-05-26T03:52:41Z","title":"Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T14:18:39.385411Z"},"links":{"citing_paper":"/paper/2505.19473"},"observation_digest":"sha256:d00702da59c59c443990df14e90b1bf4981b4e381b51595389abab1475b69109","observation_id":"3349e9be-0230-41cc-a215-33400f4858f1","resolution":{"observed_at":"2026-08-07T14:18:56.940563Z","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":"2026-08-07T14:18:56.493960Z","title":"Club: A contrastive log-ratio upper bound of mutual information","venue":null,"work_id":"7bb3ac33-b550-4b9f-a847-2c12c32f9289","year":2020},"citing_paper":{"arxiv_id":"2505.19473","last_updated":"2025-05-26T03:52:41Z","snapshot_observed_at":"2026-08-09T16:06:33.300857Z","submitted_at":"2025-05-26T03:52:41Z","title":"Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T14:18:39.536570Z"},"links":{"citing_paper":"/paper/2505.19473"},"observation_digest":"sha256:818c3588b4e922876ef44b75a974667b7cc9a7bc68eacbcf182306ca6dd478d8","observation_id":"d90ff0bc-97c4-4d58-aea1-77d6be512f4d","resolution":{"observed_at":"2026-08-07T14:18:56.599647Z","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":"2026-08-07T14:18:56.244570Z","title":"Flexibly fair representation learning by disentanglement","venue":null,"work_id":"94db9302-7895-4a8e-959a-17fda093a0d9","year":2019},"citing_paper":{"arxiv_id":"2505.19473","last_updated":"2025-05-26T03:52:41Z","snapshot_observed_at":"2026-08-09T16:06:33.300857Z","submitted_at":"2025-05-26T03:52:41Z","title":"Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T14:18:39.722314Z"},"links":{"citing_paper":"/paper/2505.19473"},"observation_digest":"sha256:278e5232e238810eb7d5eee130d516d0874ca19a1cdc28a4e889e2eb251c7717","observation_id":"f3366a5b-ee36-4208-888c-28aa11fa7d88","resolution":{"observed_at":"2026-08-07T14:18:56.343274Z","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":"2026-08-07T14:18:55.937400Z","title":"Environment inference for invariant learning","venue":null,"work_id":"0fe43837-536c-49a4-97c4-34ba085202dd","year":2021},"citing_paper":{"arxiv_id":"2505.19473","last_updated":"2025-05-26T03:52:41Z","snapshot_observed_at":"2026-08-09T16:06:33.300857Z","submitted_at":"2025-05-26T03:52:41Z","title":"Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T14:18:39.863778Z"},"links":{"citing_paper":"/paper/2505.19473"},"observation_digest":"sha256:c15c77d52049824305158119582e9a2ebc71323c28fafddb2fdd6e2a9852ca8b","observation_id":"0063d7ba-d232-47b9-8532-13f8e587a31b","resolution":{"observed_at":"2026-08-07T14:18:56.051479Z","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":"2026-08-07T14:18:40.007747Z","title":"Say no to the discrimination: Learning fair graph neural networks with limited sensitive attribute information","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.19473","last_updated":"2025-05-26T03:52:41Z","snapshot_observed_at":"2026-08-09T16:06:33.300857Z","submitted_at":"2025-05-26T03:52:41Z","title":"Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T14:18:40.007747Z"},"links":{"citing_paper":"/paper/2505.19473"},"observation_digest":"sha256:2af345d458accffd54b33dca26cca7f085a4c957123d5c4713e59ec40b594783","observation_id":"e56fa0c8-fee1-4af9-bcfc-47cc244fbef0","resolution":{"observed_at":"2026-08-07T14:18:40.007747Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:18:40.160450Z","title":"Maximum likelihood estimation of observer error-rates using the em algorithm.Journal of the Royal Statistical Society: Series C (Applied Statistics), 28(1):20–28, 1979","venue":null,"work_id":null,"year":1979},"citing_paper":{"arxiv_id":"2505.19473","last_updated":"2025-05-26T03:52:41Z","snapshot_observed_at":"2026-08-09T16:06:33.300857Z","submitted_at":"2025-05-26T03:52:41Z","title":"Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T14:18:40.160450Z"},"links":{"citing_paper":"/paper/2505.19473"},"observation_digest":"sha256:13719e9080874c8adfbfec5632f8b0cc83442461f91c7474fd5611d17bfe8068","observation_id":"409df85c-d57d-4df8-9b13-ad93ee7c8446","resolution":{"observed_at":"2026-08-07T14:18:40.160450Z","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":"2026-08-07T14:18:55.561374Z","title":"Fairness through awareness","venue":null,"work_id":"8bd7bcd3-b1ce-4bd5-80a5-29cb641d31de","year":2012},"citing_paper":{"arxiv_id":"2505.19473","last_updated":"2025-05-26T03:52:41Z","snapshot_observed_at":"2026-08-09T16:06:33.300857Z","submitted_at":"2025-05-26T03:52:41Z","title":"Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T14:18:40.296411Z"},"links":{"citing_paper":"/paper/2505.19473"},"observation_digest":"sha256:95b6e28ca481930370c6fd88c22214b455f093c2bf64fb82e34d12a6bb635f88","observation_id":"f648cf7f-f865-4b91-adc1-5cb04afa2972","resolution":{"observed_at":"2026-08-07T14:18:55.721383Z","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":"2026-08-07T14:18:55.268194Z","title":"Controllable guarantees for fair outcomes via contrastive information estimation","venue":null,"work_id":"622d93f1-35b5-439a-a41c-57c494b36b57","year":2021},"citing_paper":{"arxiv_id":"2505.19473","last_updated":"2025-05-26T03:52:41Z","snapshot_observed_at":"2026-08-09T16:06:33.300857Z","submitted_at":"2025-05-26T03:52:41Z","title":"Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T14:18:40.463443Z"},"links":{"citing_paper":"/paper/2505.19473"},"observation_digest":"sha256:08cc9e070822e7bc4c008bfd7cbd02175f1179fcb5af2f75bbacba45bc1a9894","observation_id":"f0da54eb-ec17-471b-93c6-ea67a7555263","resolution":{"observed_at":"2026-08-07T14:18:55.398944Z","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":"2026-08-07T14:18:40.567677Z","title":"Equality of opportunity in supervised learning","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2505.19473","last_updated":"2025-05-26T03:52:41Z","snapshot_observed_at":"2026-08-09T16:06:33.300857Z","submitted_at":"2025-05-26T03:52:41Z","title":"Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T14:18:40.567677Z"},"links":{"citing_paper":"/paper/2505.19473"},"observation_digest":"sha256:29f6dbbc320e67c5a7be69dbd62390251f11a27bd6d3d70c816f02c719e72134","observation_id":"ba9effe6-cce1-4a2d-a07d-08d83a245fbe","resolution":{"observed_at":"2026-08-07T14:18:40.567677Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:18:40.711521Z","title":"The movielens datasets: History and context.Acm transactions on interactive intelligent systems (tiis), 5(4):1–19, 2015","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2505.19473","last_updated":"2025-05-26T03:52:41Z","snapshot_observed_at":"2026-08-09T16:06:33.300857Z","submitted_at":"2025-05-26T03:52:41Z","title":"Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T14:18:40.711521Z"},"links":{"citing_paper":"/paper/2505.19473"},"observation_digest":"sha256:d3fb5f2bce7c5f72c79c807206e9c1435f33eff50595473160247f88cd5d62ef","observation_id":"2cbdf4bc-bb2e-445d-9473-cde968990838","resolution":{"observed_at":"2026-08-07T14:18:40.711521Z","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":"2026-08-07T14:18:54.977345Z","title":"Fairness without demographics in repeated loss minimization","venue":null,"work_id":"ebd75aa7-9a1e-47aa-8e5d-ded3eb6f8abc","year":1929},"citing_paper":{"arxiv_id":"2505.19473","last_updated":"2025-05-26T03:52:41Z","snapshot_observed_at":"2026-08-09T16:06:33.300857Z","submitted_at":"2025-05-26T03:52:41Z","title":"Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T14:18:40.877458Z"},"links":{"citing_paper":"/paper/2505.19473"},"observation_digest":"sha256:18252aa1edfc4cd72f8303ef23a9d400a9071e5934289b5c2afa13acd3f95532","observation_id":"a6ffe20a-188b-4774-99ce-5d3d80f9682e","resolution":{"observed_at":"2026-08-07T14:18:55.096676Z","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":"2026-08-07T14:18:41.007372Z","title":"Lightgcn: Simplifying and powering graph convolution network for recommendation","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.19473","last_updated":"2025-05-26T03:52:41Z","snapshot_observed_at":"2026-08-09T16:06:33.300857Z","submitted_at":"2025-05-26T03:52:41Z","title":"Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T14:18:41.007372Z"},"links":{"citing_paper":"/paper/2505.19473"},"observation_digest":"sha256:eb2650ce2631d67c49ffcb00201b7e50e8716c78a44ee6496354bfd938943b72","observation_id":"1b3a704c-bee5-4565-ba3a-d3051c0170d0","resolution":{"observed_at":"2026-08-07T14:18:41.007372Z","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":"2026-08-07T14:18:54.634109Z","title":"Music personalization at spotify","venue":null,"work_id":"ac0b5c72-6ddf-4a44-8b1d-07b43181b30a","year":2016},"citing_paper":{"arxiv_id":"2505.19473","last_updated":"2025-05-26T03:52:41Z","snapshot_observed_at":"2026-08-09T16:06:33.300857Z","submitted_at":"2025-05-26T03:52:41Z","title":"Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T14:18:41.140393Z"},"links":{"citing_paper":"/paper/2505.19473"},"observation_digest":"sha256:cda96df67c088191d37fd85028cd0a5b5ec0c79eecc94e26e5ebab6b2f4ce465","observation_id":"cdc59d78-14ec-40c1-9d7a-0e55b7091c36","resolution":{"observed_at":"2026-08-07T14:18:54.809055Z","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":"2026-08-07T14:18:54.326109Z","title":"Ir evaluation methods for retrieving highly relevant documents","venue":null,"work_id":"378fb3f6-ecb7-485f-aeac-192d5fa697ac","year":2017},"citing_paper":{"arxiv_id":"2505.19473","last_updated":"2025-05-26T03:52:41Z","snapshot_observed_at":"2026-08-09T16:06:33.300857Z","submitted_at":"2025-05-26T03:52:41Z","title":"Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T14:18:41.293454Z"},"links":{"citing_paper":"/paper/2505.19473"},"observation_digest":"sha256:45a45fa4a215b6ad7edb99f9ac201be02ab62ef278df532b3c45cb1c45e6438f","observation_id":"37fdcf13-3641-4227-8daf-65404e7622e5","resolution":{"observed_at":"2026-08-07T14:18:54.479694Z","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":"2026-08-07T14:18:53.969750Z","title":"Fairness without demographics through adversarially reweighted learning","venue":null,"work_id":"33610c20-324b-40c8-bde6-6583d3abf869","year":2020},"citing_paper":{"arxiv_id":"2505.19473","last_updated":"2025-05-26T03:52:41Z","snapshot_observed_at":"2026-08-09T16:06:33.300857Z","submitted_at":"2025-05-26T03:52:41Z","title":"Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T14:18:41.474210Z"},"links":{"citing_paper":"/paper/2505.19473"},"observation_digest":"sha256:b5c70664a69963b184ac8efc54ac57f74dbf02e23af5696386297cb9133b66b3","observation_id":"6da2e7a3-7b84-425e-83a8-a8459f0d7253","resolution":{"observed_at":"2026-08-07T14:18:54.105251Z","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":"2026-08-07T14:18:53.659438Z","title":"Algorithmic bias? an empirical study of apparent gender-based discrimination in the display of stem career ads.Management science, 65(7): 2966–2981, 2019","venue":null,"work_id":"2a72729d-a21e-42bc-9394-4d941c677219","year":2019},"citing_paper":{"arxiv_id":"2505.19473","last_updated":"2025-05-26T03:52:41Z","snapshot_observed_at":"2026-08-09T16:06:33.300857Z","submitted_at":"2025-05-26T03:52:41Z","title":"Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T14:18:41.667341Z"},"links":{"citing_paper":"/paper/2505.19473"},"observation_digest":"sha256:f9133c43a634c8f76d3ed239b12b6dd6d277817363867b06aac71a8fb9fc71cb","observation_id":"23930144-1d42-4630-b65b-29456048b1fb","resolution":{"observed_at":"2026-08-07T14:18:53.840516Z","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":"2026-08-07T14:18:53.378809Z","title":"Towards person- alized fairness based on causal notion","venue":null,"work_id":"06a57d8f-c031-4807-a0e8-1ad0c5e8c6d0","year":2021},"citing_paper":{"arxiv_id":"2505.19473","last_updated":"2025-05-26T03:52:41Z","snapshot_observed_at":"2026-08-09T16:06:33.300857Z","submitted_at":"2025-05-26T03:52:41Z","title":"Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T14:18:41.836698Z"},"links":{"citing_paper":"/paper/2505.19473"},"observation_digest":"sha256:641f6e0962bfb921aa61ae2965ee90d2260f3595c313b7a3667538f526d13417","observation_id":"5b77131c-18b1-4186-b378-7ac426466ca2","resolution":{"observed_at":"2026-08-07T14:18:53.498410Z","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":"2026-08-07T14:18:53.085194Z","title":"Llara: Large language-recommendation assistant","venue":null,"work_id":"e0eb1196-a254-4441-96c3-2445a052f43f","year":2024},"citing_paper":{"arxiv_id":"2505.19473","last_updated":"2025-05-26T03:52:41Z","snapshot_observed_at":"2026-08-09T16:06:33.300857Z","submitted_at":"2025-05-26T03:52:41Z","title":"Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T14:18:41.988090Z"},"links":{"citing_paper":"/paper/2505.19473"},"observation_digest":"sha256:b7acc9bb5ed2e3fea2a7e99f2fee3e4a6b6337b0bdd1ce0e31594adfffd2c8ee","observation_id":"18d1a3bc-fd46-40e5-930f-bf14eba9e5f7","resolution":{"observed_at":"2026-08-07T14:18:53.272259Z","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":"2026-08-07T14:18:52.747841Z","title":"Rella: Retrieval-enhanced large language models for lifelong sequential behavior comprehension in recommendation","venue":null,"work_id":"f9f767b5-a211-4cfe-ab46-f99441bfc6d6","year":2024},"citing_paper":{"arxiv_id":"2505.19473","last_updated":"2025-05-26T03:52:41Z","snapshot_observed_at":"2026-08-09T16:06:33.300857Z","submitted_at":"2025-05-26T03:52:41Z","title":"Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T14:18:42.130266Z"},"links":{"citing_paper":"/paper/2505.19473"},"observation_digest":"sha256:4c62381c2970f772eaff8867a2602ab59f3e32fb1b9ce504f42ede786840ee5c","observation_id":"7caee01b-7178-4a52-b887-a1043f39a149","resolution":{"observed_at":"2026-08-07T14:18:52.902592Z","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":"2310.01061","last_updated":"2024-02-24T03:03:12Z","snapshot_observed_at":"2026-08-14T06:53:43.346855Z","submitted_at":"2023-10-02T10:14:43Z","title":"Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.01061","snapshot_observed_at":"2026-08-07T14:18:42.245889Z","title":"Reasoning on graphs: Faithful and interpretable large language model reasoning.arXiv preprint arXiv:2310.01061, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.19473","last_updated":"2025-05-26T03:52:41Z","snapshot_observed_at":"2026-08-09T16:06:33.300857Z","submitted_at":"2025-05-26T03:52:41Z","title":"Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T14:18:42.245889Z"},"links":{"cited_paper":"/paper/2310.01061","citing_paper":"/paper/2505.19473"},"observation_digest":"sha256:c8898e36f5be5aaf6c23fc15fa17025f59808cd4487341a0d39aaeaea873d4be","observation_id":"f8be77ac-50e2-473c-bfd3-e5a1b292cf2e","resolution":{"observed_at":"2026-08-07T14:18:42.245889Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.07183","last_updated":"2022-12-01T17:38:37Z","snapshot_observed_at":"2026-08-13T14:05:48.329373Z","submitted_at":"2022-10-13T17:03:46Z","title":"Visual Classification via Description from Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.07183","snapshot_observed_at":"2026-08-07T14:18:42.396007Z","title":"Visual classification via description from large language models.arXiv preprint arXiv:2210.07183, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.19473","last_updated":"2025-05-26T03:52:41Z","snapshot_observed_at":"2026-08-09T16:06:33.300857Z","submitted_at":"2025-05-26T03:52:41Z","title":"Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T14:18:42.396007Z"},"links":{"cited_paper":"/paper/2210.07183","citing_paper":"/paper/2505.19473"},"observation_digest":"sha256:9d81c09da9e682827eddb772316f6aff70da2da949e029ca8e10135bbc2e0fb3","observation_id":"a3526ea1-c501-4ef2-83c6-287944eeb2ac","resolution":{"observed_at":"2026-08-07T14:18:42.396007Z","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":"2026-08-07T14:18:52.487745Z","title":"Invariant representations without adversarial training.Advances in neural information processing systems, 31, 2018","venue":null,"work_id":"533ad871-9a61-4c76-b33e-f95c96aa34a0","year":2018},"citing_paper":{"arxiv_id":"2505.19473","last_updated":"2025-05-26T03:52:41Z","snapshot_observed_at":"2026-08-09T16:06:33.300857Z","submitted_at":"2025-05-26T03:52:41Z","title":"Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T14:18:42.582586Z"},"links":{"citing_paper":"/paper/2505.19473"},"observation_digest":"sha256:3d1452cb81a1dd3d83bb85089a2bd7f4ca015b415309f81373c4ee843f23ac6b","observation_id":"53a0e6bc-5911-435c-a910-b54ac567a489","resolution":{"observed_at":"2026-08-07T14:18:52.588961Z","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":"2026-07-06T06:49:24.960992Z","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":"2026-08-07T14:18:42.709243Z","title":"Representation learning with contrastive predictive coding.arXiv preprint arXiv:1807.03748, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.19473","last_updated":"2025-05-26T03:52:41Z","snapshot_observed_at":"2026-08-09T16:06:33.300857Z","submitted_at":"2025-05-26T03:52:41Z","title":"Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T14:18:42.709243Z"},"links":{"cited_paper":"/paper/1807.03748","citing_paper":"/paper/2505.19473"},"observation_digest":"sha256:6007cf1daa42d0f469e57d81e0e4e901784de3f50f55ad3d3698855b803231bd","observation_id":"ed604525-b215-496a-b7ef-30303b392b51","resolution":{"observed_at":"2026-08-07T14:18:42.709243Z","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":"2026-08-07T14:18:52.235398Z","title":"A theory of justice","venue":null,"work_id":"a9708661-3f82-484d-a56c-5ab0dc32344d","year":2017},"citing_paper":{"arxiv_id":"2505.19473","last_updated":"2025-05-26T03:52:41Z","snapshot_observed_at":"2026-08-09T16:06:33.300857Z","submitted_at":"2025-05-26T03:52:41Z","title":"Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T14:18:42.871827Z"},"links":{"citing_paper":"/paper/2505.19473"},"observation_digest":"sha256:b5dab3da5802c831636e95faa4c52c2a3d14403bc18217ef4544a05c9cdf871b","observation_id":"586f91a7-61ac-440f-952b-def1aba5e19b","resolution":{"observed_at":"2026-08-07T14:18:52.336974Z","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":"2026-08-07T14:18:51.885841Z","title":"Representation learning with large language models for recommendation","venue":null,"work_id":"13af1886-8330-4025-86c4-7fc68e9f0c71","year":2024},"citing_paper":{"arxiv_id":"2505.19473","last_updated":"2025-05-26T03:52:41Z","snapshot_observed_at":"2026-08-09T16:06:33.300857Z","submitted_at":"2025-05-26T03:52:41Z","title":"Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T14:18:42.978122Z"},"links":{"citing_paper":"/paper/2505.19473"},"observation_digest":"sha256:4e7b626360453e9e89607a123bc22e328eb0af6c28aea1884cfe93478b9a3fee","observation_id":"11876559-3993-4738-a82a-5bb8a0a0cfdf","resolution":{"observed_at":"2026-08-07T14:18:52.065160Z","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":"2026-07-06T02:47:58.266745Z","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":"2026-08-07T14:18:43.149786Z","title":"Bpr: Bayesian personalized ranking from implicit feedback.arXiv preprint arXiv:1205.2618, 2012","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2505.19473","last_updated":"2025-05-26T03:52:41Z","snapshot_observed_at":"2026-08-09T16:06:33.300857Z","submitted_at":"2025-05-26T03:52:41Z","title":"Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T14:18:43.149786Z"},"links":{"cited_paper":"/paper/1205.2618","citing_paper":"/paper/2505.19473"},"observation_digest":"sha256:825738c1bed2e4ee0e3af45dd0425e04a61e6701fc7b229c1848bdb69aa21f9c","observation_id":"791aede1-670f-4251-91de-fa657c513c08","resolution":{"observed_at":"2026-08-07T14:18:43.149786Z","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":"2026-08-07T14:18:51.599686Z","title":"Leveraging large language models for multiple choice question answering","venue":null,"work_id":"38fea4fa-bdb2-4fc7-aab4-b30540776968","year":2022},"citing_paper":{"arxiv_id":"2505.19473","last_updated":"2025-05-26T03:52:41Z","snapshot_observed_at":"2026-08-09T16:06:33.300857Z","submitted_at":"2025-05-26T03:52:41Z","title":"Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T14:18:43.253223Z"},"links":{"citing_paper":"/paper/2505.19473"},"observation_digest":"sha256:c68d5e03b52b65d880680b9938ec8dae95a7d0d3ac2f31420627f46877beb347","observation_id":"febf6536-c0ee-4985-942c-536aa9310503","resolution":{"observed_at":"2026-08-07T14:18:51.764199Z","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":"2026-08-07T14:18:51.343863Z","title":"Deep learning from crowds","venue":null,"work_id":"4d732c2d-4a76-4bcc-bcfd-bd16e6e98e6a","year":2018},"citing_paper":{"arxiv_id":"2505.19473","last_updated":"2025-05-26T03:52:41Z","snapshot_observed_at":"2026-08-09T16:06:33.300857Z","submitted_at":"2025-05-26T03:52:41Z","title":"Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T14:18:43.405443Z"},"links":{"citing_paper":"/paper/2505.19473"},"observation_digest":"sha256:f2dc9594acd6bd622dd110ca4896b03d56f2b68b2b02513592755555d517e61e","observation_id":"e0731167-3fab-4e3a-990b-9d33701243d9","resolution":{"observed_at":"2026-08-07T14:18:51.457660Z","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":"1703.00810","last_updated":"2017-04-29T17:32:47Z","snapshot_observed_at":"2026-08-08T16:36:13.828301Z","submitted_at":"2017-03-02T14:53:14Z","title":"Opening the Black Box of Deep Neural Networks via Information","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1703.00810","snapshot_observed_at":"2026-08-07T14:18:43.520932Z","title":"Opening the black box of deep neural networks via information.arXiv preprint arXiv:1703.00810, 2017","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.19473","last_updated":"2025-05-26T03:52:41Z","snapshot_observed_at":"2026-08-09T16:06:33.300857Z","submitted_at":"2025-05-26T03:52:41Z","title":"Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T14:18:43.520932Z"},"links":{"cited_paper":"/paper/1703.00810","citing_paper":"/paper/2505.19473"},"observation_digest":"sha256:5af3b2c974078357a712087911879469007637acdc5eac246fb98efc33aa64db","observation_id":"bf7925ec-6f04-4b4b-a613-845eee54d7c6","resolution":{"observed_at":"2026-08-07T14:18:43.520932Z","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":"2026-08-07T14:18:51.047822Z","title":"Learning controllable fair representations","venue":null,"work_id":"7d8529dd-73b3-4e5e-84f8-d516702e3266","year":2019},"citing_paper":{"arxiv_id":"2505.19473","last_updated":"2025-05-26T03:52:41Z","snapshot_observed_at":"2026-08-09T16:06:33.300857Z","submitted_at":"2025-05-26T03:52:41Z","title":"Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-07T14:18:43.591867Z"},"links":{"citing_paper":"/paper/2505.19473"},"observation_digest":"sha256:8597fb048b556a9457509219b6816a125de845ba689906baa3c95387634d625b","observation_id":"f7c4f069-9cdc-4d77-a788-d7dc7e781b58","resolution":{"observed_at":"2026-08-07T14:18:51.219108Z","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":"2026-08-07T14:18:50.784442Z","title":"A survey on the fairness of recommender systems.ACM Transactions on Information Systems, 41(3):1–43, 2023","venue":null,"work_id":"3dc4735a-5766-4238-aa71-06adc3d78e9b","year":2023},"citing_paper":{"arxiv_id":"2505.19473","last_updated":"2025-05-26T03:52:41Z","snapshot_observed_at":"2026-08-09T16:06:33.300857Z","submitted_at":"2025-05-26T03:52:41Z","title":"Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-07T14:18:43.681117Z"},"links":{"citing_paper":"/paper/2505.19473"},"observation_digest":"sha256:c29824f8ad7615cea967937049000274a45a77cf132c7411182e73db69f3a11d","observation_id":"a3a621c4-86a7-4872-8fbc-69388a0070e3","resolution":{"observed_at":"2026-08-07T14:18:50.925188Z","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":"2026-08-07T14:18:50.461475Z","title":"Can small language models be good reasoners for sequential recommendation? InProceedings of the ACM on Web Conference 2024, pages 3876–3887, 2024","venue":null,"work_id":"efc0eb97-b231-468c-94b5-e3aed38e656b","year":2024},"citing_paper":{"arxiv_id":"2505.19473","last_updated":"2025-05-26T03:52:41Z","snapshot_observed_at":"2026-08-09T16:06:33.300857Z","submitted_at":"2025-05-26T03:52:41Z","title":"Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-07T14:18:43.755487Z"},"links":{"citing_paper":"/paper/2505.19473"},"observation_digest":"sha256:4b7942789d3d87e4ae387e489f2f7bf3094a18d11b49c37b2136e4d0c32c1934","observation_id":"2013786d-dfd1-443c-a13a-34b43b4caa51","resolution":{"observed_at":"2026-08-07T14:18:50.623372Z","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":"2026-08-07T14:18:50.138417Z","title":"Llmrec: Large language models with graph augmentation for recommendation","venue":null,"work_id":"48695bbc-6252-4f49-aa5f-e46d389fd2dc","year":2024},"citing_paper":{"arxiv_id":"2505.19473","last_updated":"2025-05-26T03:52:41Z","snapshot_observed_at":"2026-08-09T16:06:33.300857Z","submitted_at":"2025-05-26T03:52:41Z","title":"Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-07T14:18:43.850969Z"},"links":{"citing_paper":"/paper/2505.19473"},"observation_digest":"sha256:66c5680a564d2a85f2fe6c94e2cac61022f237d4c37f414e51e40d39c9c2e53d","observation_id":"98dd741d-6da7-4bb2-94a6-aa1d4d86da18","resolution":{"observed_at":"2026-08-07T14:18:50.295707Z","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":"2026-08-07T14:18:49.827285Z","title":"Learning fair representations for recommendation: A graph-based perspective","venue":null,"work_id":"47f67c63-4a4d-44c1-b2d8-7a22086bb1f5","year":2021},"citing_paper":{"arxiv_id":"2505.19473","last_updated":"2025-05-26T03:52:41Z","snapshot_observed_at":"2026-08-09T16:06:33.300857Z","submitted_at":"2025-05-26T03:52:41Z","title":"Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-07T14:18:43.973312Z"},"links":{"citing_paper":"/paper/2505.19473"},"observation_digest":"sha256:00a540dea6578ada37370fc552f155663cc2e67997c3f873762b18d697783ff2","observation_id":"e7298060-a3d3-4d59-bd7a-55487bfc9643","resolution":{"observed_at":"2026-08-07T14:18:49.949814Z","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":"2026-08-07T14:18:49.514201Z","title":"Fair class balancing: Enhancing model fairness without observing sensitive attributes","venue":null,"work_id":"b218b0c7-e0f7-4f9b-92af-78e673b2ce84","year":2020},"citing_paper":{"arxiv_id":"2505.19473","last_updated":"2025-05-26T03:52:41Z","snapshot_observed_at":"2026-08-09T16:06:33.300857Z","submitted_at":"2025-05-26T03:52:41Z","title":"Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-07T14:18:44.074882Z"},"links":{"citing_paper":"/paper/2505.19473"},"observation_digest":"sha256:f851754da049ab017b2f94a92d874b76216292c01414ce6939fa36de690897ca","observation_id":"f440982f-bdd8-46fb-a667-263a234cc7a3","resolution":{"observed_at":"2026-08-07T14:18:49.688579Z","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":"2026-08-07T14:18:49.257781Z","title":"Beyond parity: Fairness objectives for collaborative filtering","venue":null,"work_id":"7d7b7878-db0b-4d31-9fb2-11ac5a9ab345","year":2017},"citing_paper":{"arxiv_id":"2505.19473","last_updated":"2025-05-26T03:52:41Z","snapshot_observed_at":"2026-08-09T16:06:33.300857Z","submitted_at":"2025-05-26T03:52:41Z","title":"Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-07T14:18:44.184497Z"},"links":{"citing_paper":"/paper/2505.19473"},"observation_digest":"sha256:6f33c523b260e02816d5de758b5792ee57e230c745f4ea1ad76498446c208f3a","observation_id":"ad8d9b51-8828-4c6d-a14d-4471ebb9d588","resolution":{"observed_at":"2026-08-07T14:18:49.358239Z","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":"2026-08-07T14:18:48.920237Z","title":"Fair sequential recommendation without user demographics","venue":null,"work_id":"1cbdfe3a-7ab2-4011-8b3a-45b22cd8940d","year":2024},"citing_paper":{"arxiv_id":"2505.19473","last_updated":"2025-05-26T03:52:41Z","snapshot_observed_at":"2026-08-09T16:06:33.300857Z","submitted_at":"2025-05-26T03:52:41Z","title":"Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-07T14:18:44.319963Z"},"links":{"citing_paper":"/paper/2505.19473"},"observation_digest":"sha256:1ef59fdddd56cd2ee43880a13055d6ae1fe8b3015523d477edff4dbc9fddfd70","observation_id":"decc28d1-46ac-45e6-bce0-32bace1364d0","resolution":{"observed_at":"2026-08-07T14:18:49.061459Z","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":"2309.01219","last_updated":"2025-09-14T09:34:46Z","snapshot_observed_at":"2026-07-06T16:13:46.112815Z","submitted_at":"2023-09-03T16:56:48Z","title":"Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.01219","snapshot_observed_at":"2026-08-07T14:18:44.420963Z","title":"Siren’s song in the ai ocean: a survey on hallucination in large language models.arXiv preprint arXiv:2309.01219, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.19473","last_updated":"2025-05-26T03:52:41Z","snapshot_observed_at":"2026-08-09T16:06:33.300857Z","submitted_at":"2025-05-26T03:52:41Z","title":"Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-07T14:18:44.420963Z"},"links":{"cited_paper":"/paper/2309.01219","citing_paper":"/paper/2505.19473"},"observation_digest":"sha256:ea981778f46ad134ecb3d7dd870fdcd41ebe41a5758477f6a5fe152d7c248a85","observation_id":"5b637f6d-e88b-482f-ba61-a89b31c54a7b","resolution":{"observed_at":"2026-08-07T14:18:44.420963Z","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":"2026-08-07T14:18:48.615645Z","title":"Fairlisa: Fair user modeling with limited sensitive attributes information","venue":null,"work_id":"220358e6-a253-4fe5-a8d1-3fb53d241938","year":2023},"citing_paper":{"arxiv_id":"2505.19473","last_updated":"2025-05-26T03:52:41Z","snapshot_observed_at":"2026-08-09T16:06:33.300857Z","submitted_at":"2025-05-26T03:52:41Z","title":"Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-07T14:18:44.528941Z"},"links":{"citing_paper":"/paper/2505.19473"},"observation_digest":"sha256:9a1ee7bde143d4bd5355aaca88021f92518612141558177834e5b814aa1e10e7","observation_id":"216c212e-7a98-46d6-9506-f6792262e2ad","resolution":{"observed_at":"2026-08-07T14:18:48.739564Z","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":"2026-08-07T14:18:48.358275Z","title":"Fair representation learning for recommendation: A mutual information perspective","venue":null,"work_id":"bf40a8cd-1bd6-4c70-9ee7-369c45e8883a","year":2023},"citing_paper":{"arxiv_id":"2505.19473","last_updated":"2025-05-26T03:52:41Z","snapshot_observed_at":"2026-08-09T16:06:33.300857Z","submitted_at":"2025-05-26T03:52:41Z","title":"Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-07T14:18:44.691136Z"},"links":{"citing_paper":"/paper/2505.19473"},"observation_digest":"sha256:316fcc23c716848089abedaa3ab922ef71e301ea5cf031863fd217d9ec58865e","observation_id":"5ee649e4-8158-4e1b-97fc-9188ee4c794c","resolution":{"observed_at":"2026-08-07T14:18:48.486243Z","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":"2026-08-07T14:18:48.051991Z","title":"Towards fair classifiers without sensitive attributes: Exploring biases in related features","venue":null,"work_id":"3571e23b-3eae-4205-9b66-ce83870ca9f9","year":2022},"citing_paper":{"arxiv_id":"2505.19473","last_updated":"2025-05-26T03:52:41Z","snapshot_observed_at":"2026-08-09T16:06:33.300857Z","submitted_at":"2025-05-26T03:52:41Z","title":"Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-07T14:18:44.799408Z"},"links":{"citing_paper":"/paper/2505.19473"},"observation_digest":"sha256:145136adb7979bfe89ee9f713c14cc52ff288fd4a42f760b01734b57c0693915","observation_id":"f20c4d43-01e9-425d-b851-2d23e9e440a2","resolution":{"observed_at":"2026-08-07T14:18:48.159086Z","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":"2026-08-07T14:18:47.754809Z","title":"Adaptive fair representation learning for personalized fairness in recommendations via information alignment","venue":null,"work_id":"fc015c54-cdb0-4222-acfd-2cc9c8663e33","year":2024},"citing_paper":{"arxiv_id":"2505.19473","last_updated":"2025-05-26T03:52:41Z","snapshot_observed_at":"2026-08-09T16:06:33.300857Z","submitted_at":"2025-05-26T03:52:41Z","title":"Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-07T14:18:44.880519Z"},"links":{"citing_paper":"/paper/2505.19473"},"observation_digest":"sha256:fd73af430906a1a91b8b7db5fa7b5872868d90684d68af16d065482ec0021284","observation_id":"0954c3b2-36bd-459f-a32e-34e7d8bfbf10","resolution":{"observed_at":"2026-08-07T14:18:47.880149Z","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":"2026-08-07T14:18:47.496131Z","title":"Collaborative large language model for recommender systems","venue":null,"work_id":"cf36802c-3897-431e-89a7-e68c434b7540","year":2024},"citing_paper":{"arxiv_id":"2505.19473","last_updated":"2025-05-26T03:52:41Z","snapshot_observed_at":"2026-08-09T16:06:33.300857Z","submitted_at":"2025-05-26T03:52:41Z","title":"Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-07T14:18:45.011957Z"},"links":{"citing_paper":"/paper/2505.19473"},"observation_digest":"sha256:1257092ec4e96916ff1649a813408533c3f2df457c87e4ea638ae3a8bd0128a3","observation_id":"332af0e1-986b-432c-99be-d7445a50287e","resolution":{"observed_at":"2026-08-07T14:18:47.625409Z","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":"3130.6617","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:18:45.985744Z","title":"# ! \"\" #","venue":null,"work_id":"772ae3a1-ba7f-4856-8cee-acf0331fbf7f","year":2011},"citing_paper":{"arxiv_id":"2505.19473","last_updated":"2025-05-26T03:52:41Z","snapshot_observed_at":"2026-08-09T16:06:33.300857Z","submitted_at":"2025-05-26T03:52:41Z","title":"Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-07T14:18:45.175192Z"},"links":{"citing_paper":"/paper/2505.19473"},"observation_digest":"sha256:08a23da36840ddddc977e7020c899a5df1eb4bb77083e8aacf3a14ba74fa34ef","observation_id":"19356d4d-52a9-42e6-8d63-c17311539c6b","resolution":{"observed_at":"2026-08-07T14:18:46.159589Z","resolver_source":"raw_fallback","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":"2026-08-07T14:18:47.215476Z","title":"These films areoften associated with a female audienceand suggest a fondness for traditional fairy tales and romance","venue":null,"work_id":"51b362ac-d5b2-4811-afe7-918e88c948a7","year":null},"citing_paper":{"arxiv_id":"2505.19473","last_updated":"2025-05-26T03:52:41Z","snapshot_observed_at":"2026-08-09T16:06:33.300857Z","submitted_at":"2025-05-26T03:52:41Z","title":"Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-07T14:18:45.322186Z"},"links":{"citing_paper":"/paper/2505.19473"},"observation_digest":"sha256:3c93e1ce6271be17af9ab59c55825f3b20d606f187d5e02e15b6bec087ee43b7","observation_id":"0da48c19-b872-4642-956d-92050789b1ca","resolution":{"observed_at":"2026-08-07T14:18:47.350426Z","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":"2026-08-07T14:18:46.888208Z","title":null,"venue":null,"work_id":"ccebdf2d-5ef3-48b6-807b-e4c1d259fd04","year":null},"citing_paper":{"arxiv_id":"2505.19473","last_updated":"2025-05-26T03:52:41Z","snapshot_observed_at":"2026-08-09T16:06:33.300857Z","submitted_at":"2025-05-26T03:52:41Z","title":"Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-07T14:18:45.485885Z"},"links":{"citing_paper":"/paper/2505.19473"},"observation_digest":"sha256:6336070dcbe6936a8243b3f1a3597c3a00b28992639304f5044b2606479f98a4","observation_id":"9ba17c35-6609-4d72-b7f0-f3144f631550","resolution":{"observed_at":"2026-08-07T14:18:47.061634Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2026-08-07T14:18:46.624953Z","title":"4.The absence of action-oriented or sci-fi movies, which are often popular among male audiences, is a notable pattern","venue":null,"work_id":"65328237-b835-4489-be21-9c0b797c0f7b","year":null},"citing_paper":{"arxiv_id":"2505.19473","last_updated":"2025-05-26T03:52:41Z","snapshot_observed_at":"2026-08-09T16:06:33.300857Z","submitted_at":"2025-05-26T03:52:41Z","title":"Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-07T14:18:45.616916Z"},"links":{"citing_paper":"/paper/2505.19473"},"observation_digest":"sha256:fa47c77ce88bf97f46af3184847a3d4e6d376f34265ed20d9373aa76d137bc5a","observation_id":"76151ba3-7d67-483b-8482-fe0b188780c8","resolution":{"observed_at":"2026-08-07T14:18:46.778647Z","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":"2026-08-07T14:18:46.398118Z","title":"While it’s possible that a male user could have similar tastes, the consistency of these themes and patterns across the annotations suggests that the user is likely a female","venue":null,"work_id":"5d61c155-7433-4594-ae3a-1358f7bce3a9","year":null},"citing_paper":{"arxiv_id":"2505.19473","last_updated":"2025-05-26T03:52:41Z","snapshot_observed_at":"2026-08-09T16:06:33.300857Z","submitted_at":"2025-05-26T03:52:41Z","title":"Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-07T14:18:45.726648Z"},"links":{"citing_paper":"/paper/2505.19473"},"observation_digest":"sha256:fb452b8ade56dc0ac4ab58d09b54d2f240278b58e7edcdbd45b8dab17db2ea37","observation_id":"cda0f9e2-f5bb-4eed-a4bf-54a0933143a3","resolution":{"observed_at":"2026-08-07T14:18:46.473735Z","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"}}],"paper":{"arxiv_id":"2505.19473","last_updated":"2025-05-26T03:52:41Z","latest_version":1,"primary_category":"cs.IR","snapshot_observed_at":"2026-08-09T16:06:33.300857Z","submitted_at":"2025-05-26T03:52:41Z","title":"Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs"},"reference_resolution":{"displayed":54,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":13,"verified_exact":1,"verified_fuzzy":40},"total_outbound_references":54},"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 14 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 1 inbound Pith citation observation for arXiv:2505.19473."}