{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:C7WRHIE6NZIML45AJOYJZW6WUI","short_pith_number":"pith:C7WRHIE6","schema_version":"1.0","canonical_sha256":"17ed13a09e6e50c5f3a04bb09cdbd6a228138f97a0943ab45bf1d5b7a148a599","source":{"kind":"arxiv","id":"2501.15816","version":1},"attestation_state":"computed","paper":{"title":"AdaF^2M^2: Comprehensive Learning and Responsive Leveraging Features in Recommendation System","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.IR","authors_text":"Feng Zhang, Jingwu Chen, Ling Chen, Xiao Yang, Yitan Li, Yongchun Zhu, Zuotao Liu","submitted_at":"2025-01-27T06:49:27Z","abstract_excerpt":"Feature modeling, which involves feature representation learning and leveraging, plays an essential role in industrial recommendation systems. However, the data distribution in real-world applications usually follows a highly skewed long-tail pattern due to the popularity bias, which easily leads to over-reliance on ID-based features, such as user/item IDs and ID sequences of interactions. Such over-reliance makes it hard for models to learn features comprehensively, especially for those non-ID meta features, e.g., user/item characteristics. Further, it limits the feature leveraging ability in"},"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":"2501.15816","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2025-01-27T06:49:27Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"0667e26c4d4c7136706f40c31e02687e66c265892d9fd98baa867a5cbba37ac9","abstract_canon_sha256":"ca7f5ffc185d9517859f1fcf3b33ec8ed31512e7317f8b269cb4cb72b91cb4f4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:05:50.947456Z","signature_b64":"zU+OOW1OJ8Ut7pCNcn3+X0koUSGQ0fjYbwkWUcTmVtDbcRswT8oiLx9daQrTunIGZ3WfWVBaTkWNcFJHeuUWCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"17ed13a09e6e50c5f3a04bb09cdbd6a228138f97a0943ab45bf1d5b7a148a599","last_reissued_at":"2026-07-05T10:05:50.946906Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:05:50.946906Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"AdaF^2M^2: Comprehensive Learning and Responsive Leveraging Features in Recommendation System","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.IR","authors_text":"Feng Zhang, Jingwu Chen, Ling Chen, Xiao Yang, Yitan Li, Yongchun Zhu, Zuotao Liu","submitted_at":"2025-01-27T06:49:27Z","abstract_excerpt":"Feature modeling, which involves feature representation learning and leveraging, plays an essential role in industrial recommendation systems. However, the data distribution in real-world applications usually follows a highly skewed long-tail pattern due to the popularity bias, which easily leads to over-reliance on ID-based features, such as user/item IDs and ID sequences of interactions. Such over-reliance makes it hard for models to learn features comprehensively, especially for those non-ID meta features, e.g., user/item characteristics. Further, it limits the feature leveraging ability in"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.15816","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/2501.15816/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":"2501.15816","created_at":"2026-07-05T10:05:50.946980+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.15816v1","created_at":"2026-07-05T10:05:50.946980+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.15816","created_at":"2026-07-05T10:05:50.946980+00:00"},{"alias_kind":"pith_short_12","alias_value":"C7WRHIE6NZIM","created_at":"2026-07-05T10:05:50.946980+00:00"},{"alias_kind":"pith_short_16","alias_value":"C7WRHIE6NZIML45A","created_at":"2026-07-05T10:05:50.946980+00:00"},{"alias_kind":"pith_short_8","alias_value":"C7WRHIE6","created_at":"2026-07-05T10:05:50.946980+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/C7WRHIE6NZIML45AJOYJZW6WUI","json":"https://pith.science/pith/C7WRHIE6NZIML45AJOYJZW6WUI.json","graph_json":"https://pith.science/api/pith-number/C7WRHIE6NZIML45AJOYJZW6WUI/graph.json","events_json":"https://pith.science/api/pith-number/C7WRHIE6NZIML45AJOYJZW6WUI/events.json","paper":"https://pith.science/paper/C7WRHIE6"},"agent_actions":{"view_html":"https://pith.science/pith/C7WRHIE6NZIML45AJOYJZW6WUI","download_json":"https://pith.science/pith/C7WRHIE6NZIML45AJOYJZW6WUI.json","view_paper":"https://pith.science/paper/C7WRHIE6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.15816&json=true","fetch_graph":"https://pith.science/api/pith-number/C7WRHIE6NZIML45AJOYJZW6WUI/graph.json","fetch_events":"https://pith.science/api/pith-number/C7WRHIE6NZIML45AJOYJZW6WUI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/C7WRHIE6NZIML45AJOYJZW6WUI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/C7WRHIE6NZIML45AJOYJZW6WUI/action/storage_attestation","attest_author":"https://pith.science/pith/C7WRHIE6NZIML45AJOYJZW6WUI/action/author_attestation","sign_citation":"https://pith.science/pith/C7WRHIE6NZIML45AJOYJZW6WUI/action/citation_signature","submit_replication":"https://pith.science/pith/C7WRHIE6NZIML45AJOYJZW6WUI/action/replication_record"}},"created_at":"2026-07-05T10:05:50.946980+00:00","updated_at":"2026-07-05T10:05:50.946980+00:00"}