{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:VKPSLCAO33UNYGDCIPMLZZFDMK","short_pith_number":"pith:VKPSLCAO","schema_version":"1.0","canonical_sha256":"aa9f25880edee8dc186243d8bce4a362aecf97b6c692df5f2e66909de1c03067","source":{"kind":"arxiv","id":"2210.12316","version":2},"attestation_state":"computed","paper":{"title":"Learning Vector-Quantized Item Representation for Transferable Sequential Recommenders","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.IR","authors_text":"Julian McAuley, Wayne Xin Zhao, Yupeng Hou, Zhankui He","submitted_at":"2022-10-22T00:43:14Z","abstract_excerpt":"Recently, the generality of natural language text has been leveraged to develop transferable recommender systems. The basic idea is to employ pre-trained language models~(PLM) to encode item text into item representations. Despite the promising transferability, the binding between item text and item representations might be too tight, leading to potential problems such as over-emphasizing the effect of text features and exaggerating the negative impact of domain gap. To address this issue, this paper proposes VQ-Rec, a novel approach to learning Vector-Quantized item representations for transf"},"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":"2210.12316","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2022-10-22T00:43:14Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"c39424029961c62b52219baacfc9061530ed40d874d09da59d6d9288b8b5cfcc","abstract_canon_sha256":"f219efffac435be937035de44b5fb840f0a07182403d4d31d38a2be7933e0814"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:40:50.560972Z","signature_b64":"KeIPaLM+9/TjyRPNQEcbd1tDGbVJsbcjlbDy3L3wxVc2yBSE4rVrKGhtd+eMubzK+Jo3XBRDJJ//sUgCnJOfCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"aa9f25880edee8dc186243d8bce4a362aecf97b6c692df5f2e66909de1c03067","last_reissued_at":"2026-07-05T05:40:50.560390Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:40:50.560390Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning Vector-Quantized Item Representation for Transferable Sequential Recommenders","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.IR","authors_text":"Julian McAuley, Wayne Xin Zhao, Yupeng Hou, Zhankui He","submitted_at":"2022-10-22T00:43:14Z","abstract_excerpt":"Recently, the generality of natural language text has been leveraged to develop transferable recommender systems. The basic idea is to employ pre-trained language models~(PLM) to encode item text into item representations. Despite the promising transferability, the binding between item text and item representations might be too tight, leading to potential problems such as over-emphasizing the effect of text features and exaggerating the negative impact of domain gap. To address this issue, this paper proposes VQ-Rec, a novel approach to learning Vector-Quantized item representations for transf"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.12316","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/2210.12316/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":"2210.12316","created_at":"2026-07-05T05:40:50.560475+00:00"},{"alias_kind":"arxiv_version","alias_value":"2210.12316v2","created_at":"2026-07-05T05:40:50.560475+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.12316","created_at":"2026-07-05T05:40:50.560475+00:00"},{"alias_kind":"pith_short_12","alias_value":"VKPSLCAO33UN","created_at":"2026-07-05T05:40:50.560475+00:00"},{"alias_kind":"pith_short_16","alias_value":"VKPSLCAO33UNYGDC","created_at":"2026-07-05T05:40:50.560475+00:00"},{"alias_kind":"pith_short_8","alias_value":"VKPSLCAO","created_at":"2026-07-05T05:40:50.560475+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/VKPSLCAO33UNYGDCIPMLZZFDMK","json":"https://pith.science/pith/VKPSLCAO33UNYGDCIPMLZZFDMK.json","graph_json":"https://pith.science/api/pith-number/VKPSLCAO33UNYGDCIPMLZZFDMK/graph.json","events_json":"https://pith.science/api/pith-number/VKPSLCAO33UNYGDCIPMLZZFDMK/events.json","paper":"https://pith.science/paper/VKPSLCAO"},"agent_actions":{"view_html":"https://pith.science/pith/VKPSLCAO33UNYGDCIPMLZZFDMK","download_json":"https://pith.science/pith/VKPSLCAO33UNYGDCIPMLZZFDMK.json","view_paper":"https://pith.science/paper/VKPSLCAO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2210.12316&json=true","fetch_graph":"https://pith.science/api/pith-number/VKPSLCAO33UNYGDCIPMLZZFDMK/graph.json","fetch_events":"https://pith.science/api/pith-number/VKPSLCAO33UNYGDCIPMLZZFDMK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VKPSLCAO33UNYGDCIPMLZZFDMK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VKPSLCAO33UNYGDCIPMLZZFDMK/action/storage_attestation","attest_author":"https://pith.science/pith/VKPSLCAO33UNYGDCIPMLZZFDMK/action/author_attestation","sign_citation":"https://pith.science/pith/VKPSLCAO33UNYGDCIPMLZZFDMK/action/citation_signature","submit_replication":"https://pith.science/pith/VKPSLCAO33UNYGDCIPMLZZFDMK/action/replication_record"}},"created_at":"2026-07-05T05:40:50.560475+00:00","updated_at":"2026-07-05T05:40:50.560475+00:00"}