{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:3RQ22WG3NXH5ICWXZQTP3UTVPF","short_pith_number":"pith:3RQ22WG3","schema_version":"1.0","canonical_sha256":"dc61ad58db6dcfd40ad7cc26fdd2757959fb4c7216b21ba31628513fcdf27e9b","source":{"kind":"arxiv","id":"2405.17890","version":4},"attestation_state":"computed","paper":{"title":"SLMRec: Distilling Large Language Models into Small for Sequential Recommendation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.LG"],"primary_cat":"cs.IR","authors_text":"Jiaojiao Han, Qitian Wu, Wenfang Lin, Wujiang Xu, Xuying Ning, Yongfeng Zhang, Yunxiao Shi, Zujie Liang","submitted_at":"2024-05-28T07:12:06Z","abstract_excerpt":"Sequential Recommendation (SR) task involves predicting the next item a user is likely to interact with, given their past interactions. The SR models examine the sequence of a user's actions to discern more complex behavioral patterns and temporal dynamics. Recent research demonstrates the great impact of LLMs on sequential recommendation systems, either viewing sequential recommendation as language modeling or serving as the backbone for user representation. Although these methods deliver outstanding performance, there is scant evidence of the necessity of a large language model and how large"},"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":"2405.17890","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2024-05-28T07:12:06Z","cross_cats_sorted":["cs.CL","cs.LG"],"title_canon_sha256":"3223baf8b328e308d6f6d97a616bad2b1161b9eb1ce4a4fb109d9db2d0c6c827","abstract_canon_sha256":"d34ecae7921e5caabfa76353274bdfc43e3179ca39a1eaf48643c8a7f238569f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:51:22.368727Z","signature_b64":"5BCiNEsobUFQzECoVQj5dYuvhEhe557Rhlbg2OOnDd5OQM7oR3/wyf3PQJ9A7NL+hmS4Ea+k4KSSwTTFl7BdBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dc61ad58db6dcfd40ad7cc26fdd2757959fb4c7216b21ba31628513fcdf27e9b","last_reissued_at":"2026-07-05T10:51:22.368207Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:51:22.368207Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SLMRec: Distilling Large Language Models into Small for Sequential Recommendation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.LG"],"primary_cat":"cs.IR","authors_text":"Jiaojiao Han, Qitian Wu, Wenfang Lin, Wujiang Xu, Xuying Ning, Yongfeng Zhang, Yunxiao Shi, Zujie Liang","submitted_at":"2024-05-28T07:12:06Z","abstract_excerpt":"Sequential Recommendation (SR) task involves predicting the next item a user is likely to interact with, given their past interactions. The SR models examine the sequence of a user's actions to discern more complex behavioral patterns and temporal dynamics. Recent research demonstrates the great impact of LLMs on sequential recommendation systems, either viewing sequential recommendation as language modeling or serving as the backbone for user representation. Although these methods deliver outstanding performance, there is scant evidence of the necessity of a large language model and how large"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.17890","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/2405.17890/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":"2405.17890","created_at":"2026-07-05T10:51:22.368268+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.17890v4","created_at":"2026-07-05T10:51:22.368268+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.17890","created_at":"2026-07-05T10:51:22.368268+00:00"},{"alias_kind":"pith_short_12","alias_value":"3RQ22WG3NXH5","created_at":"2026-07-05T10:51:22.368268+00:00"},{"alias_kind":"pith_short_16","alias_value":"3RQ22WG3NXH5ICWX","created_at":"2026-07-05T10:51:22.368268+00:00"},{"alias_kind":"pith_short_8","alias_value":"3RQ22WG3","created_at":"2026-07-05T10:51:22.368268+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":5,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.20554","citing_title":"Structuring and Tokenizing Distributed User Interest Context for Generative Recommendation","ref_index":17,"is_internal_anchor":false},{"citing_arxiv_id":"2603.02709","citing_title":"Sensory-Aware Sequential Recommendation via Review-Distilled Representations","ref_index":28,"is_internal_anchor":false},{"citing_arxiv_id":"2604.20858","citing_title":"Mixture of Sequence: Theme-Aware Mixture-of-Experts for Long-Sequence Recommendation","ref_index":129,"is_internal_anchor":false},{"citing_arxiv_id":"2410.02644","citing_title":"Agent Security Bench (ASB): Formalizing and Benchmarking Attacks and Defenses in LLM-based Agents","ref_index":149,"is_internal_anchor":false},{"citing_arxiv_id":"2604.14922","citing_title":"LongAct: Harnessing Intrinsic Activation Patterns for Long-Context Reinforcement Learning","ref_index":30,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/3RQ22WG3NXH5ICWXZQTP3UTVPF","json":"https://pith.science/pith/3RQ22WG3NXH5ICWXZQTP3UTVPF.json","graph_json":"https://pith.science/api/pith-number/3RQ22WG3NXH5ICWXZQTP3UTVPF/graph.json","events_json":"https://pith.science/api/pith-number/3RQ22WG3NXH5ICWXZQTP3UTVPF/events.json","paper":"https://pith.science/paper/3RQ22WG3"},"agent_actions":{"view_html":"https://pith.science/pith/3RQ22WG3NXH5ICWXZQTP3UTVPF","download_json":"https://pith.science/pith/3RQ22WG3NXH5ICWXZQTP3UTVPF.json","view_paper":"https://pith.science/paper/3RQ22WG3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.17890&json=true","fetch_graph":"https://pith.science/api/pith-number/3RQ22WG3NXH5ICWXZQTP3UTVPF/graph.json","fetch_events":"https://pith.science/api/pith-number/3RQ22WG3NXH5ICWXZQTP3UTVPF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3RQ22WG3NXH5ICWXZQTP3UTVPF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3RQ22WG3NXH5ICWXZQTP3UTVPF/action/storage_attestation","attest_author":"https://pith.science/pith/3RQ22WG3NXH5ICWXZQTP3UTVPF/action/author_attestation","sign_citation":"https://pith.science/pith/3RQ22WG3NXH5ICWXZQTP3UTVPF/action/citation_signature","submit_replication":"https://pith.science/pith/3RQ22WG3NXH5ICWXZQTP3UTVPF/action/replication_record"}},"created_at":"2026-07-05T10:51:22.368268+00:00","updated_at":"2026-07-05T10:51:22.368268+00:00"}