{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:2K66B56Z74IRVXH47GCCD7VZ7H","short_pith_number":"pith:2K66B56Z","schema_version":"1.0","canonical_sha256":"d2bde0f7d9ff111adcfcf98421feb9f9fa041fdce9626ad86a7739c48825731c","source":{"kind":"arxiv","id":"2203.14257","version":1},"attestation_state":"computed","paper":{"title":"BARCOR: Towards A Unified Framework for Conversational Recommendation Systems","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Shang-Yu Su, Ting-Chun Wang, Yun-Nung Chen","submitted_at":"2022-03-27T09:42:16Z","abstract_excerpt":"Recommendation systems focus on helping users find items of interest in the situations of information overload, where users' preferences are typically estimated by the past observed behaviors. In contrast, conversational recommendation systems (CRS) aim to understand users' preferences via interactions in conversation flows. CRS is a complex problem that consists of two main tasks: (1) recommendation and (2) response generation. Previous work often tried to solve the problem in a modular manner, where recommenders and response generators are separate neural models. Such modular architectures o"},"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":"2203.14257","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2022-03-27T09:42:16Z","cross_cats_sorted":[],"title_canon_sha256":"bc9a36b37143e0be0deca87e819ca0787ed59c2ef277f683f3d6a068bbc48845","abstract_canon_sha256":"e880e339b3746b7fdbd3cd12021b1bebaa75daa4e810b1324fed0beea61f251d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:08:49.303580Z","signature_b64":"0fiHYzSCExNxqBPzOvtvrHHo1YtcxpZJXFy/qjwNxCi77aACGLjzonooWZFnhGzM3s+W4zTeyyPd/VCKHPL3CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d2bde0f7d9ff111adcfcf98421feb9f9fa041fdce9626ad86a7739c48825731c","last_reissued_at":"2026-07-05T04:08:49.303159Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:08:49.303159Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"BARCOR: Towards A Unified Framework for Conversational Recommendation Systems","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Shang-Yu Su, Ting-Chun Wang, Yun-Nung Chen","submitted_at":"2022-03-27T09:42:16Z","abstract_excerpt":"Recommendation systems focus on helping users find items of interest in the situations of information overload, where users' preferences are typically estimated by the past observed behaviors. In contrast, conversational recommendation systems (CRS) aim to understand users' preferences via interactions in conversation flows. CRS is a complex problem that consists of two main tasks: (1) recommendation and (2) response generation. Previous work often tried to solve the problem in a modular manner, where recommenders and response generators are separate neural models. Such modular architectures o"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.14257","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/2203.14257/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":"2203.14257","created_at":"2026-07-05T04:08:49.303220+00:00"},{"alias_kind":"arxiv_version","alias_value":"2203.14257v1","created_at":"2026-07-05T04:08:49.303220+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.14257","created_at":"2026-07-05T04:08:49.303220+00:00"},{"alias_kind":"pith_short_12","alias_value":"2K66B56Z74IR","created_at":"2026-07-05T04:08:49.303220+00:00"},{"alias_kind":"pith_short_16","alias_value":"2K66B56Z74IRVXH4","created_at":"2026-07-05T04:08:49.303220+00:00"},{"alias_kind":"pith_short_8","alias_value":"2K66B56Z","created_at":"2026-07-05T04:08:49.303220+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.13053","citing_title":"A Standardized Re-evaluation of Conversational Recommender Systems on the ReDial Dataset","ref_index":14,"is_internal_anchor":false},{"citing_arxiv_id":"2605.13053","citing_title":"A Standardized Re-evaluation of Conversational Recommender Systems on the ReDial Dataset","ref_index":14,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/2K66B56Z74IRVXH47GCCD7VZ7H","json":"https://pith.science/pith/2K66B56Z74IRVXH47GCCD7VZ7H.json","graph_json":"https://pith.science/api/pith-number/2K66B56Z74IRVXH47GCCD7VZ7H/graph.json","events_json":"https://pith.science/api/pith-number/2K66B56Z74IRVXH47GCCD7VZ7H/events.json","paper":"https://pith.science/paper/2K66B56Z"},"agent_actions":{"view_html":"https://pith.science/pith/2K66B56Z74IRVXH47GCCD7VZ7H","download_json":"https://pith.science/pith/2K66B56Z74IRVXH47GCCD7VZ7H.json","view_paper":"https://pith.science/paper/2K66B56Z","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2203.14257&json=true","fetch_graph":"https://pith.science/api/pith-number/2K66B56Z74IRVXH47GCCD7VZ7H/graph.json","fetch_events":"https://pith.science/api/pith-number/2K66B56Z74IRVXH47GCCD7VZ7H/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2K66B56Z74IRVXH47GCCD7VZ7H/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2K66B56Z74IRVXH47GCCD7VZ7H/action/storage_attestation","attest_author":"https://pith.science/pith/2K66B56Z74IRVXH47GCCD7VZ7H/action/author_attestation","sign_citation":"https://pith.science/pith/2K66B56Z74IRVXH47GCCD7VZ7H/action/citation_signature","submit_replication":"https://pith.science/pith/2K66B56Z74IRVXH47GCCD7VZ7H/action/replication_record"}},"created_at":"2026-07-05T04:08:49.303220+00:00","updated_at":"2026-07-05T04:08:49.303220+00:00"}