{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:H3KGYJYJYA6ZZYC63RIT4TWVBK","short_pith_number":"pith:H3KGYJYJ","schema_version":"1.0","canonical_sha256":"3ed46c2709c03d9ce05edc513e4ed50aab73679204989e463fc06bb6028e442a","source":{"kind":"arxiv","id":"2502.09046","version":1},"attestation_state":"computed","paper":{"title":"Criteria-Aware Graph Filtering: Extremely Fast Yet Accurate Multi-Criteria Recommendation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.IT","cs.LG","cs.SI","math.IT"],"primary_cat":"cs.IR","authors_text":"Jaemin Yoo, Jin-Duk Park, Won-Yong Shin","submitted_at":"2025-02-13T08:01:38Z","abstract_excerpt":"Multi-criteria (MC) recommender systems, which utilize MC rating information for recommendation, are increasingly widespread in various e-commerce domains. However, the MC recommendation using training-based collaborative filtering, requiring consideration of multiple ratings compared to single-criterion counterparts, often poses practical challenges in achieving state-of-the-art performance along with scalable model training. To solve this problem, we propose CA-GF, a training-free MC recommendation method, which is built upon criteria-aware graph filtering for efficient yet accurate MC recom"},"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":"2502.09046","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2025-02-13T08:01:38Z","cross_cats_sorted":["cs.AI","cs.IT","cs.LG","cs.SI","math.IT"],"title_canon_sha256":"8ed0aeef51203e79f9bc25bfb527b8427d06f4b261376150b4b8594a47b2e590","abstract_canon_sha256":"9997a132b54a4950cfa823dc9d8aa7d2936f78ec62a5163abd83ad58ce90c342"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:13:51.657280Z","signature_b64":"63jue9T/o9XdMs2uk5XP+RwzqxCaWCWhhxQXYypZhnpX9WfORqpLTY7+KNqxDLJubb35EN6XdAA9aennAIYSBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3ed46c2709c03d9ce05edc513e4ed50aab73679204989e463fc06bb6028e442a","last_reissued_at":"2026-07-05T10:13:51.656899Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:13:51.656899Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Criteria-Aware Graph Filtering: Extremely Fast Yet Accurate Multi-Criteria Recommendation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.IT","cs.LG","cs.SI","math.IT"],"primary_cat":"cs.IR","authors_text":"Jaemin Yoo, Jin-Duk Park, Won-Yong Shin","submitted_at":"2025-02-13T08:01:38Z","abstract_excerpt":"Multi-criteria (MC) recommender systems, which utilize MC rating information for recommendation, are increasingly widespread in various e-commerce domains. However, the MC recommendation using training-based collaborative filtering, requiring consideration of multiple ratings compared to single-criterion counterparts, often poses practical challenges in achieving state-of-the-art performance along with scalable model training. To solve this problem, we propose CA-GF, a training-free MC recommendation method, which is built upon criteria-aware graph filtering for efficient yet accurate MC recom"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.09046","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/2502.09046/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":"2502.09046","created_at":"2026-07-05T10:13:51.656949+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.09046v1","created_at":"2026-07-05T10:13:51.656949+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.09046","created_at":"2026-07-05T10:13:51.656949+00:00"},{"alias_kind":"pith_short_12","alias_value":"H3KGYJYJYA6Z","created_at":"2026-07-05T10:13:51.656949+00:00"},{"alias_kind":"pith_short_16","alias_value":"H3KGYJYJYA6ZZYC6","created_at":"2026-07-05T10:13:51.656949+00:00"},{"alias_kind":"pith_short_8","alias_value":"H3KGYJYJ","created_at":"2026-07-05T10:13:51.656949+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/H3KGYJYJYA6ZZYC63RIT4TWVBK","json":"https://pith.science/pith/H3KGYJYJYA6ZZYC63RIT4TWVBK.json","graph_json":"https://pith.science/api/pith-number/H3KGYJYJYA6ZZYC63RIT4TWVBK/graph.json","events_json":"https://pith.science/api/pith-number/H3KGYJYJYA6ZZYC63RIT4TWVBK/events.json","paper":"https://pith.science/paper/H3KGYJYJ"},"agent_actions":{"view_html":"https://pith.science/pith/H3KGYJYJYA6ZZYC63RIT4TWVBK","download_json":"https://pith.science/pith/H3KGYJYJYA6ZZYC63RIT4TWVBK.json","view_paper":"https://pith.science/paper/H3KGYJYJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.09046&json=true","fetch_graph":"https://pith.science/api/pith-number/H3KGYJYJYA6ZZYC63RIT4TWVBK/graph.json","fetch_events":"https://pith.science/api/pith-number/H3KGYJYJYA6ZZYC63RIT4TWVBK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/H3KGYJYJYA6ZZYC63RIT4TWVBK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/H3KGYJYJYA6ZZYC63RIT4TWVBK/action/storage_attestation","attest_author":"https://pith.science/pith/H3KGYJYJYA6ZZYC63RIT4TWVBK/action/author_attestation","sign_citation":"https://pith.science/pith/H3KGYJYJYA6ZZYC63RIT4TWVBK/action/citation_signature","submit_replication":"https://pith.science/pith/H3KGYJYJYA6ZZYC63RIT4TWVBK/action/replication_record"}},"created_at":"2026-07-05T10:13:51.656949+00:00","updated_at":"2026-07-05T10:13:51.656949+00:00"}