{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:JGUZ4FXQ5I2DO6TTILZCOIJGYT","short_pith_number":"pith:JGUZ4FXQ","schema_version":"1.0","canonical_sha256":"49a99e16f0ea34377a7342f2272126c4c158ad8027f64bd4bf9bd1de11be574a","source":{"kind":"arxiv","id":"2409.11699","version":2},"attestation_state":"computed","paper":{"title":"FLARE: Fusing Language Models and Collaborative Architectures for Recommender Enhancement","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.IR","authors_text":"Ambarish Jash, Hubert Pham, James Pine, Krishna Sayana, Liam Hebert, Marialena Kyriakidi, Sukhdeep Sodhi","submitted_at":"2024-09-18T04:43:41Z","abstract_excerpt":"Recent proposals in recommender systems represent items with their textual description, using a large language model. They show better results on standard benchmarks compared to an item ID-only model, such as Bert4Rec. In this work, we revisit the often-used Bert4Rec baseline and show that with further tuning, Bert4Rec significantly outperforms previously reported numbers, and in some datasets, is competitive with state-of-the-art models.\n  With revised baselines for item ID-only models, this paper also establishes new competitive results for architectures that combine IDs and textual descript"},"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":"2409.11699","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2024-09-18T04:43:41Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"13c4c404ab15912c82c0c1abfa56cdc6904cdb810297f71e8f1f476def1a1d99","abstract_canon_sha256":"27a544085012f38130a415bc86f1ca7090e7c7ea3b9097107de584dc817517bb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:25:10.172912Z","signature_b64":"X0ITlRSWVmjJ/9+yKydGg5T/mAjz0h/yOno7YBGoz1vmhL7og6WlsLq6S1wr39qfOMqpU6aykUGvIKJyR7o9CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"49a99e16f0ea34377a7342f2272126c4c158ad8027f64bd4bf9bd1de11be574a","last_reissued_at":"2026-07-05T10:25:10.172443Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:25:10.172443Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"FLARE: Fusing Language Models and Collaborative Architectures for Recommender Enhancement","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.IR","authors_text":"Ambarish Jash, Hubert Pham, James Pine, Krishna Sayana, Liam Hebert, Marialena Kyriakidi, Sukhdeep Sodhi","submitted_at":"2024-09-18T04:43:41Z","abstract_excerpt":"Recent proposals in recommender systems represent items with their textual description, using a large language model. They show better results on standard benchmarks compared to an item ID-only model, such as Bert4Rec. In this work, we revisit the often-used Bert4Rec baseline and show that with further tuning, Bert4Rec significantly outperforms previously reported numbers, and in some datasets, is competitive with state-of-the-art models.\n  With revised baselines for item ID-only models, this paper also establishes new competitive results for architectures that combine IDs and textual descript"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.11699","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/2409.11699/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":"2409.11699","created_at":"2026-07-05T10:25:10.172500+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.11699v2","created_at":"2026-07-05T10:25:10.172500+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.11699","created_at":"2026-07-05T10:25:10.172500+00:00"},{"alias_kind":"pith_short_12","alias_value":"JGUZ4FXQ5I2D","created_at":"2026-07-05T10:25:10.172500+00:00"},{"alias_kind":"pith_short_16","alias_value":"JGUZ4FXQ5I2DO6TT","created_at":"2026-07-05T10:25:10.172500+00:00"},{"alias_kind":"pith_short_8","alias_value":"JGUZ4FXQ","created_at":"2026-07-05T10:25:10.172500+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.13432","citing_title":"Large Language Model Enhanced Recommender Systems: A Survey","ref_index":21,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JGUZ4FXQ5I2DO6TTILZCOIJGYT","json":"https://pith.science/pith/JGUZ4FXQ5I2DO6TTILZCOIJGYT.json","graph_json":"https://pith.science/api/pith-number/JGUZ4FXQ5I2DO6TTILZCOIJGYT/graph.json","events_json":"https://pith.science/api/pith-number/JGUZ4FXQ5I2DO6TTILZCOIJGYT/events.json","paper":"https://pith.science/paper/JGUZ4FXQ"},"agent_actions":{"view_html":"https://pith.science/pith/JGUZ4FXQ5I2DO6TTILZCOIJGYT","download_json":"https://pith.science/pith/JGUZ4FXQ5I2DO6TTILZCOIJGYT.json","view_paper":"https://pith.science/paper/JGUZ4FXQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.11699&json=true","fetch_graph":"https://pith.science/api/pith-number/JGUZ4FXQ5I2DO6TTILZCOIJGYT/graph.json","fetch_events":"https://pith.science/api/pith-number/JGUZ4FXQ5I2DO6TTILZCOIJGYT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JGUZ4FXQ5I2DO6TTILZCOIJGYT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JGUZ4FXQ5I2DO6TTILZCOIJGYT/action/storage_attestation","attest_author":"https://pith.science/pith/JGUZ4FXQ5I2DO6TTILZCOIJGYT/action/author_attestation","sign_citation":"https://pith.science/pith/JGUZ4FXQ5I2DO6TTILZCOIJGYT/action/citation_signature","submit_replication":"https://pith.science/pith/JGUZ4FXQ5I2DO6TTILZCOIJGYT/action/replication_record"}},"created_at":"2026-07-05T10:25:10.172500+00:00","updated_at":"2026-07-05T10:25:10.172500+00:00"}