{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:XVCWIS3VDUYLTY77YMRMZDOSUW","short_pith_number":"pith:XVCWIS3V","schema_version":"1.0","canonical_sha256":"bd45644b751d30b9e3ffc322cc8dd2a592fbf6f1775ab48c891a77a1a96ba8c9","source":{"kind":"arxiv","id":"2305.03624","version":1},"attestation_state":"computed","paper":{"title":"Retraining A Graph-based Recommender with Interests Disentanglement","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Aixin Sun, Jie Zhang, Yitong Ji","submitted_at":"2023-05-05T15:36:33Z","abstract_excerpt":"In a practical recommender system, new interactions are continuously observed. Some interactions are expected, because they largely follow users' long-term preferences. Some other interactions are indications of recent trends in user preference changes or marketing positions of new items. Accordingly, the recommender needs to be periodically retrained or updated to capture the new trends, and yet not to forget the long-term preferences. In this paper, we propose a novel and generic retraining framework called Disentangled Incremental Learning (DIL) for graph-based recommenders. We assume that "},"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":"2305.03624","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2023-05-05T15:36:33Z","cross_cats_sorted":[],"title_canon_sha256":"79fe231e89defc9af9fbc0d97fdb7e16290d4ad4a8ea418bbc4e5aff0c2e89d5","abstract_canon_sha256":"46072d53c119d37c001cb6dda5fd08b6c6593ea97ed44596403f8c18e69563c1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:07:25.413529Z","signature_b64":"pDUzxb99xaioAqW+vRDu5SR9yQO3iUsx+9CidnkDH/miUDqElUtvZnIyVCzLCp8ntTFqy5IsnyZaYfkHtVIDCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bd45644b751d30b9e3ffc322cc8dd2a592fbf6f1775ab48c891a77a1a96ba8c9","last_reissued_at":"2026-07-05T06:07:25.413072Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:07:25.413072Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Retraining A Graph-based Recommender with Interests Disentanglement","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Aixin Sun, Jie Zhang, Yitong Ji","submitted_at":"2023-05-05T15:36:33Z","abstract_excerpt":"In a practical recommender system, new interactions are continuously observed. Some interactions are expected, because they largely follow users' long-term preferences. Some other interactions are indications of recent trends in user preference changes or marketing positions of new items. Accordingly, the recommender needs to be periodically retrained or updated to capture the new trends, and yet not to forget the long-term preferences. In this paper, we propose a novel and generic retraining framework called Disentangled Incremental Learning (DIL) for graph-based recommenders. We assume that "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.03624","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/2305.03624/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":"2305.03624","created_at":"2026-07-05T06:07:25.413130+00:00"},{"alias_kind":"arxiv_version","alias_value":"2305.03624v1","created_at":"2026-07-05T06:07:25.413130+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.03624","created_at":"2026-07-05T06:07:25.413130+00:00"},{"alias_kind":"pith_short_12","alias_value":"XVCWIS3VDUYL","created_at":"2026-07-05T06:07:25.413130+00:00"},{"alias_kind":"pith_short_16","alias_value":"XVCWIS3VDUYLTY77","created_at":"2026-07-05T06:07:25.413130+00:00"},{"alias_kind":"pith_short_8","alias_value":"XVCWIS3V","created_at":"2026-07-05T06:07:25.413130+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/XVCWIS3VDUYLTY77YMRMZDOSUW","json":"https://pith.science/pith/XVCWIS3VDUYLTY77YMRMZDOSUW.json","graph_json":"https://pith.science/api/pith-number/XVCWIS3VDUYLTY77YMRMZDOSUW/graph.json","events_json":"https://pith.science/api/pith-number/XVCWIS3VDUYLTY77YMRMZDOSUW/events.json","paper":"https://pith.science/paper/XVCWIS3V"},"agent_actions":{"view_html":"https://pith.science/pith/XVCWIS3VDUYLTY77YMRMZDOSUW","download_json":"https://pith.science/pith/XVCWIS3VDUYLTY77YMRMZDOSUW.json","view_paper":"https://pith.science/paper/XVCWIS3V","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2305.03624&json=true","fetch_graph":"https://pith.science/api/pith-number/XVCWIS3VDUYLTY77YMRMZDOSUW/graph.json","fetch_events":"https://pith.science/api/pith-number/XVCWIS3VDUYLTY77YMRMZDOSUW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XVCWIS3VDUYLTY77YMRMZDOSUW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XVCWIS3VDUYLTY77YMRMZDOSUW/action/storage_attestation","attest_author":"https://pith.science/pith/XVCWIS3VDUYLTY77YMRMZDOSUW/action/author_attestation","sign_citation":"https://pith.science/pith/XVCWIS3VDUYLTY77YMRMZDOSUW/action/citation_signature","submit_replication":"https://pith.science/pith/XVCWIS3VDUYLTY77YMRMZDOSUW/action/replication_record"}},"created_at":"2026-07-05T06:07:25.413130+00:00","updated_at":"2026-07-05T06:07:25.413130+00:00"}