{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:E5FCOGPEQ5QLF74HTOE65XS6XK","short_pith_number":"pith:E5FCOGPE","schema_version":"1.0","canonical_sha256":"274a2719e48760b2ff879b89eede5ebaa33b84efc18747c783dbce23f71d45d3","source":{"kind":"arxiv","id":"2407.05441","version":4},"attestation_state":"computed","paper":{"title":"Language Representations Can be What Recommenders Need: Findings and Potentials","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.IR","authors_text":"An Zhang, Leheng Sheng, Tat-Seng Chua, Xiang Wang, Yi Zhang, Yuxin Chen","submitted_at":"2024-07-07T17:05:24Z","abstract_excerpt":"Recent studies empirically indicate that language models (LMs) encode rich world knowledge beyond mere semantics, attracting significant attention across various fields. However, in the recommendation domain, it remains uncertain whether LMs implicitly encode user preference information. Contrary to prevailing understanding that LMs and traditional recommenders learn two distinct representation spaces due to the huge gap in language and behavior modeling objectives, this work re-examines such understanding and explores extracting a recommendation space directly from the language representation"},"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":"2407.05441","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2024-07-07T17:05:24Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"f1ff3e8588f3db9d2d6dc4cda1985320fa1c31f670a849a7f0f33dc99b4581bd","abstract_canon_sha256":"52300e0a306fb2d6c77b0dc66268e7276983739a4fe41469f03fb5d87409538c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:51:23.756853Z","signature_b64":"cHKM1z6cIBOwOLnnVeUf3vFEiSpV8RFJEWZkAvclLiTh4F6dhWpUhkifYX/zT4jjqgdBQnHInYt7ZMBF9WzEBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"274a2719e48760b2ff879b89eede5ebaa33b84efc18747c783dbce23f71d45d3","last_reissued_at":"2026-07-05T10:51:23.756344Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:51:23.756344Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Language Representations Can be What Recommenders Need: Findings and Potentials","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.IR","authors_text":"An Zhang, Leheng Sheng, Tat-Seng Chua, Xiang Wang, Yi Zhang, Yuxin Chen","submitted_at":"2024-07-07T17:05:24Z","abstract_excerpt":"Recent studies empirically indicate that language models (LMs) encode rich world knowledge beyond mere semantics, attracting significant attention across various fields. However, in the recommendation domain, it remains uncertain whether LMs implicitly encode user preference information. Contrary to prevailing understanding that LMs and traditional recommenders learn two distinct representation spaces due to the huge gap in language and behavior modeling objectives, this work re-examines such understanding and explores extracting a recommendation space directly from the language representation"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.05441","kind":"arxiv","version":4},"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/2407.05441/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":"2407.05441","created_at":"2026-07-05T10:51:23.756398+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.05441v4","created_at":"2026-07-05T10:51:23.756398+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.05441","created_at":"2026-07-05T10:51:23.756398+00:00"},{"alias_kind":"pith_short_12","alias_value":"E5FCOGPEQ5QL","created_at":"2026-07-05T10:51:23.756398+00:00"},{"alias_kind":"pith_short_16","alias_value":"E5FCOGPEQ5QLF74H","created_at":"2026-07-05T10:51:23.756398+00:00"},{"alias_kind":"pith_short_8","alias_value":"E5FCOGPE","created_at":"2026-07-05T10:51:23.756398+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2601.02366","citing_title":"TextBridgeGNN: Pre-training Graph Neural Network for Cross-Domain Recommendation via Text-Guided Transfer","ref_index":29,"is_internal_anchor":false},{"citing_arxiv_id":"2512.21863","citing_title":"Frozen LVLMs for Micro-Video Recommendation: A Systematic Study of Feature Extraction and Fusion","ref_index":43,"is_internal_anchor":false},{"citing_arxiv_id":"2604.23578","citing_title":"LLMs Reading the Rhythms of Daily Life: Aligned Understanding for Behavior Prediction and Generation","ref_index":5,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/E5FCOGPEQ5QLF74HTOE65XS6XK","json":"https://pith.science/pith/E5FCOGPEQ5QLF74HTOE65XS6XK.json","graph_json":"https://pith.science/api/pith-number/E5FCOGPEQ5QLF74HTOE65XS6XK/graph.json","events_json":"https://pith.science/api/pith-number/E5FCOGPEQ5QLF74HTOE65XS6XK/events.json","paper":"https://pith.science/paper/E5FCOGPE"},"agent_actions":{"view_html":"https://pith.science/pith/E5FCOGPEQ5QLF74HTOE65XS6XK","download_json":"https://pith.science/pith/E5FCOGPEQ5QLF74HTOE65XS6XK.json","view_paper":"https://pith.science/paper/E5FCOGPE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.05441&json=true","fetch_graph":"https://pith.science/api/pith-number/E5FCOGPEQ5QLF74HTOE65XS6XK/graph.json","fetch_events":"https://pith.science/api/pith-number/E5FCOGPEQ5QLF74HTOE65XS6XK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/E5FCOGPEQ5QLF74HTOE65XS6XK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/E5FCOGPEQ5QLF74HTOE65XS6XK/action/storage_attestation","attest_author":"https://pith.science/pith/E5FCOGPEQ5QLF74HTOE65XS6XK/action/author_attestation","sign_citation":"https://pith.science/pith/E5FCOGPEQ5QLF74HTOE65XS6XK/action/citation_signature","submit_replication":"https://pith.science/pith/E5FCOGPEQ5QLF74HTOE65XS6XK/action/replication_record"}},"created_at":"2026-07-05T10:51:23.756398+00:00","updated_at":"2026-07-05T10:51:23.756398+00:00"}