{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:24TCHHKR7FGERLTOHE74TKVGCY","short_pith_number":"pith:24TCHHKR","schema_version":"1.0","canonical_sha256":"d726239d51f94c48ae6e393fc9aaa61628ce8160cdb090d2570506b7955e9635","source":{"kind":"arxiv","id":"2301.08143","version":2},"attestation_state":"computed","paper":{"title":"Dual Personalization on Federated Recommendation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.IR","authors_text":"Bo Yang, Chengqi Zhang, Chunxu Zhang, Guodong Long, Peng Yan, Tianyi Zhou, Zijian Zhang","submitted_at":"2023-01-16T05:26:07Z","abstract_excerpt":"Federated recommendation is a new Internet service architecture that aims to provide privacy-preserving recommendation services in federated settings. Existing solutions are used to combine distributed recommendation algorithms and privacy-preserving mechanisms. Thus it inherently takes the form of heavyweight models at the server and hinders the deployment of on-device intelligent models to end-users. This paper proposes a novel Personalized Federated Recommendation (PFedRec) framework to learn many user-specific lightweight models to be deployed on smart devices rather than a heavyweight mod"},"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":"2301.08143","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2023-01-16T05:26:07Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"5b619966a13931cd56068792de9b7475b3e560b56aa7c4a6558a294dd14ae2bc","abstract_canon_sha256":"d6fbc17bddbeba8d118addbf7c6f5477b9e20baa6eb53534d6c3f9035b95fc55"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:09:47.772528Z","signature_b64":"avIk98lYIgjpfwXfUw2T/7r8e25RYnhgR331zdt+b8mj4dsgr0MayeL6Hn/QPPXh85fICtd/v/1ycPTZeWVNAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d726239d51f94c48ae6e393fc9aaa61628ce8160cdb090d2570506b7955e9635","last_reissued_at":"2026-07-05T06:09:47.772070Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:09:47.772070Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Dual Personalization on Federated Recommendation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.IR","authors_text":"Bo Yang, Chengqi Zhang, Chunxu Zhang, Guodong Long, Peng Yan, Tianyi Zhou, Zijian Zhang","submitted_at":"2023-01-16T05:26:07Z","abstract_excerpt":"Federated recommendation is a new Internet service architecture that aims to provide privacy-preserving recommendation services in federated settings. Existing solutions are used to combine distributed recommendation algorithms and privacy-preserving mechanisms. Thus it inherently takes the form of heavyweight models at the server and hinders the deployment of on-device intelligent models to end-users. This paper proposes a novel Personalized Federated Recommendation (PFedRec) framework to learn many user-specific lightweight models to be deployed on smart devices rather than a heavyweight mod"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2301.08143","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/2301.08143/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":"2301.08143","created_at":"2026-07-05T06:09:47.772130+00:00"},{"alias_kind":"arxiv_version","alias_value":"2301.08143v2","created_at":"2026-07-05T06:09:47.772130+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2301.08143","created_at":"2026-07-05T06:09:47.772130+00:00"},{"alias_kind":"pith_short_12","alias_value":"24TCHHKR7FGE","created_at":"2026-07-05T06:09:47.772130+00:00"},{"alias_kind":"pith_short_16","alias_value":"24TCHHKR7FGERLTO","created_at":"2026-07-05T06:09:47.772130+00:00"},{"alias_kind":"pith_short_8","alias_value":"24TCHHKR","created_at":"2026-07-05T06:09:47.772130+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.11433","citing_title":"FedMM: Federated Collaborative Signal Quantization for Multi-Market CTR Prediction","ref_index":41,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/24TCHHKR7FGERLTOHE74TKVGCY","json":"https://pith.science/pith/24TCHHKR7FGERLTOHE74TKVGCY.json","graph_json":"https://pith.science/api/pith-number/24TCHHKR7FGERLTOHE74TKVGCY/graph.json","events_json":"https://pith.science/api/pith-number/24TCHHKR7FGERLTOHE74TKVGCY/events.json","paper":"https://pith.science/paper/24TCHHKR"},"agent_actions":{"view_html":"https://pith.science/pith/24TCHHKR7FGERLTOHE74TKVGCY","download_json":"https://pith.science/pith/24TCHHKR7FGERLTOHE74TKVGCY.json","view_paper":"https://pith.science/paper/24TCHHKR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2301.08143&json=true","fetch_graph":"https://pith.science/api/pith-number/24TCHHKR7FGERLTOHE74TKVGCY/graph.json","fetch_events":"https://pith.science/api/pith-number/24TCHHKR7FGERLTOHE74TKVGCY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/24TCHHKR7FGERLTOHE74TKVGCY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/24TCHHKR7FGERLTOHE74TKVGCY/action/storage_attestation","attest_author":"https://pith.science/pith/24TCHHKR7FGERLTOHE74TKVGCY/action/author_attestation","sign_citation":"https://pith.science/pith/24TCHHKR7FGERLTOHE74TKVGCY/action/citation_signature","submit_replication":"https://pith.science/pith/24TCHHKR7FGERLTOHE74TKVGCY/action/replication_record"}},"created_at":"2026-07-05T06:09:47.772130+00:00","updated_at":"2026-07-05T06:09:47.772130+00:00"}