{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:QQBNIT5Z3BIWQJ2D2WXQVBT5W3","short_pith_number":"pith:QQBNIT5Z","schema_version":"1.0","canonical_sha256":"8402d44fb9d851682743d5af0a867db6f046d0872b21baf4cd3d04aaaa43b77a","source":{"kind":"arxiv","id":"2001.04830","version":1},"attestation_state":"computed","paper":{"title":"Sequential Recommender Systems: Challenges, Progress and Prospects","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.IR","authors_text":"Liang Hu, Longbing Cao, Mehmet Orgun, Quan Z. Sheng, Shoujin Wang, Yan Wang","submitted_at":"2019-12-28T05:12:28Z","abstract_excerpt":"The emerging topic of sequential recommender systems has attracted increasing attention in recent years.Different from the conventional recommender systems including collaborative filtering and content-based filtering, SRSs try to understand and model the sequential user behaviors, the interactions between users and items, and the evolution of users preferences and item popularity over time. SRSs involve the above aspects for more precise characterization of user contexts, intent and goals, and item consumption trend, leading to more accurate, customized and dynamic recommendations.In this pap"},"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":"2001.04830","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2019-12-28T05:12:28Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"5c5c452e15e0bc73af37818ecb651dc23b6ffa68bf5bf9a1d56aefe59cdfbe9c","abstract_canon_sha256":"cc8a9a8ba8cfe111af570bea3272774fabc8a166184e1daab2f0daf468b89e84"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:33:30.099549Z","signature_b64":"m558OWxo4zwtvuQ0Fk2jI8PRGq5Ys4MZiK57Ee3mVMTjAhgQlR5r2wOC1EilopLviMGz0EYh1VqYHWDPleMMCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8402d44fb9d851682743d5af0a867db6f046d0872b21baf4cd3d04aaaa43b77a","last_reissued_at":"2026-07-05T00:33:30.099066Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:33:30.099066Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Sequential Recommender Systems: Challenges, Progress and Prospects","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.IR","authors_text":"Liang Hu, Longbing Cao, Mehmet Orgun, Quan Z. Sheng, Shoujin Wang, Yan Wang","submitted_at":"2019-12-28T05:12:28Z","abstract_excerpt":"The emerging topic of sequential recommender systems has attracted increasing attention in recent years.Different from the conventional recommender systems including collaborative filtering and content-based filtering, SRSs try to understand and model the sequential user behaviors, the interactions between users and items, and the evolution of users preferences and item popularity over time. SRSs involve the above aspects for more precise characterization of user contexts, intent and goals, and item consumption trend, leading to more accurate, customized and dynamic recommendations.In this pap"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2001.04830","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/2001.04830/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":"2001.04830","created_at":"2026-07-05T00:33:30.099123+00:00"},{"alias_kind":"arxiv_version","alias_value":"2001.04830v1","created_at":"2026-07-05T00:33:30.099123+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2001.04830","created_at":"2026-07-05T00:33:30.099123+00:00"},{"alias_kind":"pith_short_12","alias_value":"QQBNIT5Z3BIW","created_at":"2026-07-05T00:33:30.099123+00:00"},{"alias_kind":"pith_short_16","alias_value":"QQBNIT5Z3BIWQJ2D","created_at":"2026-07-05T00:33:30.099123+00:00"},{"alias_kind":"pith_short_8","alias_value":"QQBNIT5Z","created_at":"2026-07-05T00:33:30.099123+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.29141","citing_title":"Toward User Preference Alignment in LLM Recommendation via Explicit Context Feedback","ref_index":38,"is_internal_anchor":false},{"citing_arxiv_id":"2502.16759","citing_title":"Can Explanations Improve Recommendations? Evidence from Prediction-Informed Explanations","ref_index":89,"is_internal_anchor":false},{"citing_arxiv_id":"2511.19931","citing_title":"LLM-EDT: Large Language Model Enhanced Cross-domain Sequential Recommendation with Dual-phase Training","ref_index":27,"is_internal_anchor":false},{"citing_arxiv_id":"2604.23810","citing_title":"Similar Users-Augmented Interest Network","ref_index":50,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QQBNIT5Z3BIWQJ2D2WXQVBT5W3","json":"https://pith.science/pith/QQBNIT5Z3BIWQJ2D2WXQVBT5W3.json","graph_json":"https://pith.science/api/pith-number/QQBNIT5Z3BIWQJ2D2WXQVBT5W3/graph.json","events_json":"https://pith.science/api/pith-number/QQBNIT5Z3BIWQJ2D2WXQVBT5W3/events.json","paper":"https://pith.science/paper/QQBNIT5Z"},"agent_actions":{"view_html":"https://pith.science/pith/QQBNIT5Z3BIWQJ2D2WXQVBT5W3","download_json":"https://pith.science/pith/QQBNIT5Z3BIWQJ2D2WXQVBT5W3.json","view_paper":"https://pith.science/paper/QQBNIT5Z","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2001.04830&json=true","fetch_graph":"https://pith.science/api/pith-number/QQBNIT5Z3BIWQJ2D2WXQVBT5W3/graph.json","fetch_events":"https://pith.science/api/pith-number/QQBNIT5Z3BIWQJ2D2WXQVBT5W3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QQBNIT5Z3BIWQJ2D2WXQVBT5W3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QQBNIT5Z3BIWQJ2D2WXQVBT5W3/action/storage_attestation","attest_author":"https://pith.science/pith/QQBNIT5Z3BIWQJ2D2WXQVBT5W3/action/author_attestation","sign_citation":"https://pith.science/pith/QQBNIT5Z3BIWQJ2D2WXQVBT5W3/action/citation_signature","submit_replication":"https://pith.science/pith/QQBNIT5Z3BIWQJ2D2WXQVBT5W3/action/replication_record"}},"created_at":"2026-07-05T00:33:30.099123+00:00","updated_at":"2026-07-05T00:33:30.099123+00:00"}