{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:4HBA243DK276AZICEA56THLIHY","short_pith_number":"pith:4HBA243D","schema_version":"1.0","canonical_sha256":"e1c20d736356bfe06502203be99d683e1fc58e74f3fa8d7d6b679b72c03d80dc","source":{"kind":"arxiv","id":"2402.18166","version":1},"attestation_state":"computed","paper":{"title":"Sequence-level Semantic Representation Fusion for Recommender Systems","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Bingqian Li, Jinpeng Wang, Junjie Zhang, Lanling Xu, Mingchen Cai, Wayne Xin Zhao, Zhen Tian","submitted_at":"2024-02-28T08:55:20Z","abstract_excerpt":"With the rapid development of recommender systems, there is increasing side information that can be employed to improve the recommendation performance. Specially, we focus on the utilization of the associated \\emph{textual data} of items (eg product title) and study how text features can be effectively fused with ID features in sequential recommendation. However, there exists distinct data characteristics for the two kinds of item features, making a direct fusion method (eg adding text and ID embeddings as item representation) become less effective. To address this issue, we propose a novel {\\"},"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":"2402.18166","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2024-02-28T08:55:20Z","cross_cats_sorted":[],"title_canon_sha256":"77e523cde316c8766dba03ee9fba99f16e1654b561d60f457b390692172a5614","abstract_canon_sha256":"60f1c7de664066676c4cbe2a09439fc789dbd56bf759bdf4d153bd90a68621e7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:50:13.243296Z","signature_b64":"L1RxSH6bgZxvAndBpD29huNBtKiarjInasZhDTFhc4mv1Ww0eyWUWZTOx9PI5LMDDQjpE+Z7RYafHX/8L6rzCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e1c20d736356bfe06502203be99d683e1fc58e74f3fa8d7d6b679b72c03d80dc","last_reissued_at":"2026-07-05T07:50:13.242821Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:50:13.242821Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Sequence-level Semantic Representation Fusion for Recommender Systems","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Bingqian Li, Jinpeng Wang, Junjie Zhang, Lanling Xu, Mingchen Cai, Wayne Xin Zhao, Zhen Tian","submitted_at":"2024-02-28T08:55:20Z","abstract_excerpt":"With the rapid development of recommender systems, there is increasing side information that can be employed to improve the recommendation performance. Specially, we focus on the utilization of the associated \\emph{textual data} of items (eg product title) and study how text features can be effectively fused with ID features in sequential recommendation. However, there exists distinct data characteristics for the two kinds of item features, making a direct fusion method (eg adding text and ID embeddings as item representation) become less effective. To address this issue, we propose a novel {\\"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.18166","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/2402.18166/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":"2402.18166","created_at":"2026-07-05T07:50:13.242878+00:00"},{"alias_kind":"arxiv_version","alias_value":"2402.18166v1","created_at":"2026-07-05T07:50:13.242878+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.18166","created_at":"2026-07-05T07:50:13.242878+00:00"},{"alias_kind":"pith_short_12","alias_value":"4HBA243DK276","created_at":"2026-07-05T07:50:13.242878+00:00"},{"alias_kind":"pith_short_16","alias_value":"4HBA243DK276AZIC","created_at":"2026-07-05T07:50:13.242878+00:00"},{"alias_kind":"pith_short_8","alias_value":"4HBA243D","created_at":"2026-07-05T07:50:13.242878+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2501.14269","citing_title":"Hierarchical Time-Aware Mixture of Experts for Multi-Modal Sequential Recommendation","ref_index":57,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/4HBA243DK276AZICEA56THLIHY","json":"https://pith.science/pith/4HBA243DK276AZICEA56THLIHY.json","graph_json":"https://pith.science/api/pith-number/4HBA243DK276AZICEA56THLIHY/graph.json","events_json":"https://pith.science/api/pith-number/4HBA243DK276AZICEA56THLIHY/events.json","paper":"https://pith.science/paper/4HBA243D"},"agent_actions":{"view_html":"https://pith.science/pith/4HBA243DK276AZICEA56THLIHY","download_json":"https://pith.science/pith/4HBA243DK276AZICEA56THLIHY.json","view_paper":"https://pith.science/paper/4HBA243D","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2402.18166&json=true","fetch_graph":"https://pith.science/api/pith-number/4HBA243DK276AZICEA56THLIHY/graph.json","fetch_events":"https://pith.science/api/pith-number/4HBA243DK276AZICEA56THLIHY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4HBA243DK276AZICEA56THLIHY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4HBA243DK276AZICEA56THLIHY/action/storage_attestation","attest_author":"https://pith.science/pith/4HBA243DK276AZICEA56THLIHY/action/author_attestation","sign_citation":"https://pith.science/pith/4HBA243DK276AZICEA56THLIHY/action/citation_signature","submit_replication":"https://pith.science/pith/4HBA243DK276AZICEA56THLIHY/action/replication_record"}},"created_at":"2026-07-05T07:50:13.242878+00:00","updated_at":"2026-07-05T07:50:13.242878+00:00"}