{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:BMKN3GPHQQG4OFR2MCV4RA4B57","short_pith_number":"pith:BMKN3GPH","schema_version":"1.0","canonical_sha256":"0b14dd99e7840dc7163a60abc88381efc492dbe8b25e95238d332d53a82f6403","source":{"kind":"arxiv","id":"2308.04706","version":2},"attestation_state":"computed","paper":{"title":"Pareto Invariant Representation Learning for Multimedia Recommendation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.IR","authors_text":"Chunyuan Zheng, Haoxuan Li, Li Liu, Qingsong Li, Shanshan Huang","submitted_at":"2023-08-09T04:57:56Z","abstract_excerpt":"Multimedia recommendation involves personalized ranking tasks, where multimedia content is usually represented using a generic encoder. However, these generic representations introduce spurious correlations that fail to reveal users' true preferences. Existing works attempt to alleviate this problem by learning invariant representations, but overlook the balance between independent and identically distributed (IID) and out-of-distribution (OOD) generalization. In this paper, we propose a framework called Pareto Invariant Representation Learning (PaInvRL) to mitigate the impact of spurious corr"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2308.04706","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2023-08-09T04:57:56Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"41b6b1477ba570cf9679d24ede56539c7a11d89be5a9d88d7a11eb654a10c922","abstract_canon_sha256":"6ee76bfd321cf6bda9f3a76b3fe0aa3d4f536bdc0382b047716d486616fc721f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:44:08.853400Z","signature_b64":"vr/y3PRlmqW9SzDmzIKp/XaCuACwx0pJBmkUYhtxLogfVyilny1EX4odp0QrY3asILeIGbrQfYPdSAv+YZGVDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0b14dd99e7840dc7163a60abc88381efc492dbe8b25e95238d332d53a82f6403","last_reissued_at":"2026-07-05T06:44:08.852845Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:44:08.852845Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Pareto Invariant Representation Learning for Multimedia Recommendation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.IR","authors_text":"Chunyuan Zheng, Haoxuan Li, Li Liu, Qingsong Li, Shanshan Huang","submitted_at":"2023-08-09T04:57:56Z","abstract_excerpt":"Multimedia recommendation involves personalized ranking tasks, where multimedia content is usually represented using a generic encoder. However, these generic representations introduce spurious correlations that fail to reveal users' true preferences. Existing works attempt to alleviate this problem by learning invariant representations, but overlook the balance between independent and identically distributed (IID) and out-of-distribution (OOD) generalization. In this paper, we propose a framework called Pareto Invariant Representation Learning (PaInvRL) to mitigate the impact of spurious corr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.04706","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2308.04706/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2308.04706","created_at":"2026-07-05T06:44:08.852908+00:00"},{"alias_kind":"arxiv_version","alias_value":"2308.04706v2","created_at":"2026-07-05T06:44:08.852908+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.04706","created_at":"2026-07-05T06:44:08.852908+00:00"},{"alias_kind":"pith_short_12","alias_value":"BMKN3GPHQQG4","created_at":"2026-07-05T06:44:08.852908+00:00"},{"alias_kind":"pith_short_16","alias_value":"BMKN3GPHQQG4OFR2","created_at":"2026-07-05T06:44:08.852908+00:00"},{"alias_kind":"pith_short_8","alias_value":"BMKN3GPH","created_at":"2026-07-05T06:44:08.852908+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2501.12175","citing_title":"Less is More: Information Bottleneck Denoised Multimedia Recommendation","ref_index":20,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/BMKN3GPHQQG4OFR2MCV4RA4B57","json":"https://pith.science/pith/BMKN3GPHQQG4OFR2MCV4RA4B57.json","graph_json":"https://pith.science/api/pith-number/BMKN3GPHQQG4OFR2MCV4RA4B57/graph.json","events_json":"https://pith.science/api/pith-number/BMKN3GPHQQG4OFR2MCV4RA4B57/events.json","paper":"https://pith.science/paper/BMKN3GPH"},"agent_actions":{"view_html":"https://pith.science/pith/BMKN3GPHQQG4OFR2MCV4RA4B57","download_json":"https://pith.science/pith/BMKN3GPHQQG4OFR2MCV4RA4B57.json","view_paper":"https://pith.science/paper/BMKN3GPH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2308.04706&json=true","fetch_graph":"https://pith.science/api/pith-number/BMKN3GPHQQG4OFR2MCV4RA4B57/graph.json","fetch_events":"https://pith.science/api/pith-number/BMKN3GPHQQG4OFR2MCV4RA4B57/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BMKN3GPHQQG4OFR2MCV4RA4B57/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BMKN3GPHQQG4OFR2MCV4RA4B57/action/storage_attestation","attest_author":"https://pith.science/pith/BMKN3GPHQQG4OFR2MCV4RA4B57/action/author_attestation","sign_citation":"https://pith.science/pith/BMKN3GPHQQG4OFR2MCV4RA4B57/action/citation_signature","submit_replication":"https://pith.science/pith/BMKN3GPHQQG4OFR2MCV4RA4B57/action/replication_record"}},"created_at":"2026-07-05T06:44:08.852908+00:00","updated_at":"2026-07-05T06:44:08.852908+00:00"}