{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:RTWDT45G6AME2RZGLLR3B2TFWX","short_pith_number":"pith:RTWDT45G","schema_version":"1.0","canonical_sha256":"8cec39f3a6f0184d47265ae3b0ea65b5fba73a34187a51506c78b507b8e7b2d9","source":{"kind":"arxiv","id":"2412.13544","version":1},"attestation_state":"computed","paper":{"title":"Bridging the User-side Knowledge Gap in Knowledge-aware Recommendations with Large Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.IR","authors_text":"Fuji Ren, Jiawen Deng, Satoshi Nakagawa, Shimin Cai, Tao Zhou, Zhe Li, Zheng Hu, Ziyun Jiao","submitted_at":"2024-12-18T06:43:56Z","abstract_excerpt":"In recent years, knowledge graphs have been integrated into recommender systems as item-side auxiliary information, enhancing recommendation accuracy. However, constructing and integrating structural user-side knowledge remains a significant challenge due to the improper granularity and inherent scarcity of user-side features. Recent advancements in Large Language Models (LLMs) offer the potential to bridge this gap by leveraging their human behavior understanding and extensive real-world knowledge. Nevertheless, integrating LLM-generated information into recommender systems presents challenge"},"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":"2412.13544","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2024-12-18T06:43:56Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"abb476af3bbbe2a1f820cbc7f0a36505836d53879f559b5238e9b406173d6520","abstract_canon_sha256":"21b5e6e8d0d88ceddae9cad027208ab94506e811c65566f61b1313b76d98d700"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:51:03.703248Z","signature_b64":"ieEN1215KKlFufMXG6JUxwzrrJ+NSYeJy7+ggDItIP8/AxaqiQxCSLmFuq5LLhNoZWgXm4pDqcADxETRduhwAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8cec39f3a6f0184d47265ae3b0ea65b5fba73a34187a51506c78b507b8e7b2d9","last_reissued_at":"2026-07-05T09:51:03.702742Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:51:03.702742Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Bridging the User-side Knowledge Gap in Knowledge-aware Recommendations with Large Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.IR","authors_text":"Fuji Ren, Jiawen Deng, Satoshi Nakagawa, Shimin Cai, Tao Zhou, Zhe Li, Zheng Hu, Ziyun Jiao","submitted_at":"2024-12-18T06:43:56Z","abstract_excerpt":"In recent years, knowledge graphs have been integrated into recommender systems as item-side auxiliary information, enhancing recommendation accuracy. However, constructing and integrating structural user-side knowledge remains a significant challenge due to the improper granularity and inherent scarcity of user-side features. Recent advancements in Large Language Models (LLMs) offer the potential to bridge this gap by leveraging their human behavior understanding and extensive real-world knowledge. Nevertheless, integrating LLM-generated information into recommender systems presents challenge"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.13544","kind":"arxiv","version":1},"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/2412.13544/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":"2412.13544","created_at":"2026-07-05T09:51:03.702807+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.13544v1","created_at":"2026-07-05T09:51:03.702807+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.13544","created_at":"2026-07-05T09:51:03.702807+00:00"},{"alias_kind":"pith_short_12","alias_value":"RTWDT45G6AME","created_at":"2026-07-05T09:51:03.702807+00:00"},{"alias_kind":"pith_short_16","alias_value":"RTWDT45G6AME2RZG","created_at":"2026-07-05T09:51:03.702807+00:00"},{"alias_kind":"pith_short_8","alias_value":"RTWDT45G","created_at":"2026-07-05T09:51:03.702807+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2508.11194","citing_title":"Representation Quantization for Collaborative Filtering Augmentation","ref_index":14,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/RTWDT45G6AME2RZGLLR3B2TFWX","json":"https://pith.science/pith/RTWDT45G6AME2RZGLLR3B2TFWX.json","graph_json":"https://pith.science/api/pith-number/RTWDT45G6AME2RZGLLR3B2TFWX/graph.json","events_json":"https://pith.science/api/pith-number/RTWDT45G6AME2RZGLLR3B2TFWX/events.json","paper":"https://pith.science/paper/RTWDT45G"},"agent_actions":{"view_html":"https://pith.science/pith/RTWDT45G6AME2RZGLLR3B2TFWX","download_json":"https://pith.science/pith/RTWDT45G6AME2RZGLLR3B2TFWX.json","view_paper":"https://pith.science/paper/RTWDT45G","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.13544&json=true","fetch_graph":"https://pith.science/api/pith-number/RTWDT45G6AME2RZGLLR3B2TFWX/graph.json","fetch_events":"https://pith.science/api/pith-number/RTWDT45G6AME2RZGLLR3B2TFWX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RTWDT45G6AME2RZGLLR3B2TFWX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RTWDT45G6AME2RZGLLR3B2TFWX/action/storage_attestation","attest_author":"https://pith.science/pith/RTWDT45G6AME2RZGLLR3B2TFWX/action/author_attestation","sign_citation":"https://pith.science/pith/RTWDT45G6AME2RZGLLR3B2TFWX/action/citation_signature","submit_replication":"https://pith.science/pith/RTWDT45G6AME2RZGLLR3B2TFWX/action/replication_record"}},"created_at":"2026-07-05T09:51:03.702807+00:00","updated_at":"2026-07-05T09:51:03.702807+00:00"}