{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:JPBFNJUMH4RF25NLPR7CWRA77S","short_pith_number":"pith:JPBFNJUM","schema_version":"1.0","canonical_sha256":"4bc256a68c3f225d75ab7c7e2b441ffc88eb8881086b10520831ba9d7d79b729","source":{"kind":"arxiv","id":"2403.03900","version":2},"attestation_state":"computed","paper":{"title":"Mamba4Rec: Towards Efficient Sequential Recommendation with Selective State Space Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Chengkai Liu, Hanzhou Liu, James Caverlee, Jianghao Lin, Jianling Wang","submitted_at":"2024-03-06T18:00:15Z","abstract_excerpt":"Sequential recommendation aims to estimate the dynamic user preferences and sequential dependencies among historical user behaviors. Although Transformer-based models have proven to be effective for sequential recommendation, they suffer from the inference inefficiency problem stemming from the quadratic computational complexity of attention operators, especially for long behavior sequences. Inspired by the recent success of state space models (SSMs), we propose Mamba4Rec, which is the first work to explore the potential of selective SSMs for efficient sequential recommendation. Built upon the"},"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":"2403.03900","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2024-03-06T18:00:15Z","cross_cats_sorted":[],"title_canon_sha256":"578cda418a51f3b52886f656debf90017eb4b9a40baab3641a0b5324668922bd","abstract_canon_sha256":"6c6393f8cd1f481cbea54193f2277276d1d92aad67f3c311ed6e1686d584a5af"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:38:07.006496Z","signature_b64":"fcZ8nA2xc2IzDGHk7PUB3r1Mshk8abtFz7FrUdj2FTJiiScP5j1oLZAHi7A52zNsuVrsEGl7mII2pztm8qRXBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4bc256a68c3f225d75ab7c7e2b441ffc88eb8881086b10520831ba9d7d79b729","last_reissued_at":"2026-07-05T08:38:07.005979Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:38:07.005979Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Mamba4Rec: Towards Efficient Sequential Recommendation with Selective State Space Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Chengkai Liu, Hanzhou Liu, James Caverlee, Jianghao Lin, Jianling Wang","submitted_at":"2024-03-06T18:00:15Z","abstract_excerpt":"Sequential recommendation aims to estimate the dynamic user preferences and sequential dependencies among historical user behaviors. Although Transformer-based models have proven to be effective for sequential recommendation, they suffer from the inference inefficiency problem stemming from the quadratic computational complexity of attention operators, especially for long behavior sequences. Inspired by the recent success of state space models (SSMs), we propose Mamba4Rec, which is the first work to explore the potential of selective SSMs for efficient sequential recommendation. Built upon the"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.03900","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/2403.03900/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":"2403.03900","created_at":"2026-07-05T08:38:07.006048+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.03900v2","created_at":"2026-07-05T08:38:07.006048+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.03900","created_at":"2026-07-05T08:38:07.006048+00:00"},{"alias_kind":"pith_short_12","alias_value":"JPBFNJUMH4RF","created_at":"2026-07-05T08:38:07.006048+00:00"},{"alias_kind":"pith_short_16","alias_value":"JPBFNJUMH4RF25NL","created_at":"2026-07-05T08:38:07.006048+00:00"},{"alias_kind":"pith_short_8","alias_value":"JPBFNJUM","created_at":"2026-07-05T08:38:07.006048+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":11,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.25329","citing_title":"State Space Models Meet Remote Sensing: A Survey","ref_index":157,"is_internal_anchor":false},{"citing_arxiv_id":"2606.17707","citing_title":"Do Generative Recommenders Deepen the Information Cocoon? A Closed-Loop Simulation with LLM-powered User Simulators","ref_index":24,"is_internal_anchor":false},{"citing_arxiv_id":"2606.09888","citing_title":"SinkRec: Mitigating Semantic State Sink in Long Sequence Recommendation with Memory-Conditioned Gated Delta Networks","ref_index":16,"is_internal_anchor":false},{"citing_arxiv_id":"2606.02602","citing_title":"Graph Mamba Survival Analysis Based on Topology-Aware ordering","ref_index":61,"is_internal_anchor":false},{"citing_arxiv_id":"2512.13368","citing_title":"BlossomRec: Block-level Fused Sparse Attention Mechanism for Sequential Recommendations","ref_index":47,"is_internal_anchor":false},{"citing_arxiv_id":"2408.01129","citing_title":"A Survey of Mamba","ref_index":118,"is_internal_anchor":false},{"citing_arxiv_id":"2605.19651","citing_title":"Divergence Meets Consensus: A Multi-Source Negative Sampling Framework for Sequential Recommendation","ref_index":21,"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":74,"is_internal_anchor":false},{"citing_arxiv_id":"2605.01726","citing_title":"FEDIN: Frequency-Enhanced Deep Interest Network for Click-Through Rate Prediction","ref_index":12,"is_internal_anchor":false},{"citing_arxiv_id":"2604.18146","citing_title":"Modular Representation Compression: Adapting LLMs for Efficient and Effective Recommendations","ref_index":38,"is_internal_anchor":false},{"citing_arxiv_id":"2605.06981","citing_title":"Bridging Textual Profiles and Latent User Embeddings for Personalization","ref_index":10,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JPBFNJUMH4RF25NLPR7CWRA77S","json":"https://pith.science/pith/JPBFNJUMH4RF25NLPR7CWRA77S.json","graph_json":"https://pith.science/api/pith-number/JPBFNJUMH4RF25NLPR7CWRA77S/graph.json","events_json":"https://pith.science/api/pith-number/JPBFNJUMH4RF25NLPR7CWRA77S/events.json","paper":"https://pith.science/paper/JPBFNJUM"},"agent_actions":{"view_html":"https://pith.science/pith/JPBFNJUMH4RF25NLPR7CWRA77S","download_json":"https://pith.science/pith/JPBFNJUMH4RF25NLPR7CWRA77S.json","view_paper":"https://pith.science/paper/JPBFNJUM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.03900&json=true","fetch_graph":"https://pith.science/api/pith-number/JPBFNJUMH4RF25NLPR7CWRA77S/graph.json","fetch_events":"https://pith.science/api/pith-number/JPBFNJUMH4RF25NLPR7CWRA77S/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JPBFNJUMH4RF25NLPR7CWRA77S/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JPBFNJUMH4RF25NLPR7CWRA77S/action/storage_attestation","attest_author":"https://pith.science/pith/JPBFNJUMH4RF25NLPR7CWRA77S/action/author_attestation","sign_citation":"https://pith.science/pith/JPBFNJUMH4RF25NLPR7CWRA77S/action/citation_signature","submit_replication":"https://pith.science/pith/JPBFNJUMH4RF25NLPR7CWRA77S/action/replication_record"}},"created_at":"2026-07-05T08:38:07.006048+00:00","updated_at":"2026-07-05T08:38:07.006048+00:00"}