{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:PXDSD7QOVNOAVVO7RKA5T6QMTG","short_pith_number":"pith:PXDSD7QO","schema_version":"1.0","canonical_sha256":"7dc721fe0eab5c0ad5df8a81d9fa0c999e355d2c9d393de3d80c8ddad56777e1","source":{"kind":"arxiv","id":"2310.20453","version":1},"attestation_state":"computed","paper":{"title":"Generate What You Prefer: Reshaping Sequential Recommendation via Guided Diffusion","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Jiancan Wu, Xiangnan He, Xiang Wang, Yancheng Yuan, Zhengyi Yang, Zhicai Wang","submitted_at":"2023-10-31T13:45:39Z","abstract_excerpt":"Sequential recommendation aims to recommend the next item that matches a user's interest, based on the sequence of items he/she interacted with before. Scrutinizing previous studies, we can summarize a common learning-to-classify paradigm -- given a positive item, a recommender model performs negative sampling to add negative items and learns to classify whether the user prefers them or not, based on his/her historical interaction sequence. Although effective, we reveal two inherent limitations:(1) it may differ from human behavior in that a user could imagine an oracle item in mind and select"},"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":"2310.20453","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2023-10-31T13:45:39Z","cross_cats_sorted":[],"title_canon_sha256":"3001aeb80933ac1212bced4e9c5be03a3e3dbc71da935973d8e18a6feb6c4b9e","abstract_canon_sha256":"0c24f865ebea77a90ab5d3c869352f73f1292dcb197618a19719ba208b41ebbf"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:07:30.429817Z","signature_b64":"dz37IThY6x5qfLD7ME2l2g4XsmWyXX0YdGkAJqe1/vWL8HCmTZQezw+opNjS/NlkbMBiS9KnpX3t7Lqks0RwAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7dc721fe0eab5c0ad5df8a81d9fa0c999e355d2c9d393de3d80c8ddad56777e1","last_reissued_at":"2026-07-05T07:07:30.429325Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:07:30.429325Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Generate What You Prefer: Reshaping Sequential Recommendation via Guided Diffusion","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Jiancan Wu, Xiangnan He, Xiang Wang, Yancheng Yuan, Zhengyi Yang, Zhicai Wang","submitted_at":"2023-10-31T13:45:39Z","abstract_excerpt":"Sequential recommendation aims to recommend the next item that matches a user's interest, based on the sequence of items he/she interacted with before. Scrutinizing previous studies, we can summarize a common learning-to-classify paradigm -- given a positive item, a recommender model performs negative sampling to add negative items and learns to classify whether the user prefers them or not, based on his/her historical interaction sequence. Although effective, we reveal two inherent limitations:(1) it may differ from human behavior in that a user could imagine an oracle item in mind and select"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.20453","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/2310.20453/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":"2310.20453","created_at":"2026-07-05T07:07:30.429390+00:00"},{"alias_kind":"arxiv_version","alias_value":"2310.20453v1","created_at":"2026-07-05T07:07:30.429390+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.20453","created_at":"2026-07-05T07:07:30.429390+00:00"},{"alias_kind":"pith_short_12","alias_value":"PXDSD7QOVNOA","created_at":"2026-07-05T07:07:30.429390+00:00"},{"alias_kind":"pith_short_16","alias_value":"PXDSD7QOVNOAVVO7","created_at":"2026-07-05T07:07:30.429390+00:00"},{"alias_kind":"pith_short_8","alias_value":"PXDSD7QO","created_at":"2026-07-05T07:07:30.429390+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.23702","citing_title":"TubiFM: Unified Item, Carousel, and Search Ranking for Streaming Discovery","ref_index":23,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/PXDSD7QOVNOAVVO7RKA5T6QMTG","json":"https://pith.science/pith/PXDSD7QOVNOAVVO7RKA5T6QMTG.json","graph_json":"https://pith.science/api/pith-number/PXDSD7QOVNOAVVO7RKA5T6QMTG/graph.json","events_json":"https://pith.science/api/pith-number/PXDSD7QOVNOAVVO7RKA5T6QMTG/events.json","paper":"https://pith.science/paper/PXDSD7QO"},"agent_actions":{"view_html":"https://pith.science/pith/PXDSD7QOVNOAVVO7RKA5T6QMTG","download_json":"https://pith.science/pith/PXDSD7QOVNOAVVO7RKA5T6QMTG.json","view_paper":"https://pith.science/paper/PXDSD7QO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2310.20453&json=true","fetch_graph":"https://pith.science/api/pith-number/PXDSD7QOVNOAVVO7RKA5T6QMTG/graph.json","fetch_events":"https://pith.science/api/pith-number/PXDSD7QOVNOAVVO7RKA5T6QMTG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PXDSD7QOVNOAVVO7RKA5T6QMTG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PXDSD7QOVNOAVVO7RKA5T6QMTG/action/storage_attestation","attest_author":"https://pith.science/pith/PXDSD7QOVNOAVVO7RKA5T6QMTG/action/author_attestation","sign_citation":"https://pith.science/pith/PXDSD7QOVNOAVVO7RKA5T6QMTG/action/citation_signature","submit_replication":"https://pith.science/pith/PXDSD7QOVNOAVVO7RKA5T6QMTG/action/replication_record"}},"created_at":"2026-07-05T07:07:30.429390+00:00","updated_at":"2026-07-05T07:07:30.429390+00:00"}