{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:CHE6NTLUBXN3AMMX24DJOAXGAK","short_pith_number":"pith:CHE6NTLU","schema_version":"1.0","canonical_sha256":"11c9e6cd740ddbb03197d7069702e60284884a7167b6c2d23dbde4628dcd9031","source":{"kind":"arxiv","id":"2012.11842","version":1},"attestation_state":"computed","paper":{"title":"Personalized Adaptive Meta Learning for Cold-start User Preference Prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.IR","authors_text":"Bo An, Qingwen Liu, Runsheng Yu, Wenwu Ou, Xu He, Yu Gong, Yu Zhu","submitted_at":"2020-12-22T05:48:08Z","abstract_excerpt":"A common challenge in personalized user preference prediction is the cold-start problem. Due to the lack of user-item interactions, directly learning from the new users' log data causes serious over-fitting problem. Recently, many existing studies regard the cold-start personalized preference prediction as a few-shot learning problem, where each user is the task and recommended items are the classes, and the gradient-based meta learning method (MAML) is leveraged to address this challenge. However, in real-world application, the users are not uniformly distributed (i.e., different users may ha"},"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":"2012.11842","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2020-12-22T05:48:08Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"7794dc89bb7318d962fe842204cd513153f46c7fafff2ab1a5d76ac4ef719beb","abstract_canon_sha256":"70bfd2af748748b8231991e3eb67e5ed8a971075f7fbe0f3878fe120c41c50d5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:01:21.830347Z","signature_b64":"sksoqYa+sbvcO66z9umbCX9D0ZLKCh7/z6Xh0/3dWRO/dn1ZyRwTT1gh6MAi76etZJ0PWptCNcMh4fd5l3wjBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"11c9e6cd740ddbb03197d7069702e60284884a7167b6c2d23dbde4628dcd9031","last_reissued_at":"2026-07-05T02:01:21.829989Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:01:21.829989Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Personalized Adaptive Meta Learning for Cold-start User Preference Prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.IR","authors_text":"Bo An, Qingwen Liu, Runsheng Yu, Wenwu Ou, Xu He, Yu Gong, Yu Zhu","submitted_at":"2020-12-22T05:48:08Z","abstract_excerpt":"A common challenge in personalized user preference prediction is the cold-start problem. Due to the lack of user-item interactions, directly learning from the new users' log data causes serious over-fitting problem. Recently, many existing studies regard the cold-start personalized preference prediction as a few-shot learning problem, where each user is the task and recommended items are the classes, and the gradient-based meta learning method (MAML) is leveraged to address this challenge. However, in real-world application, the users are not uniformly distributed (i.e., different users may ha"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2012.11842","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/2012.11842/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":"2012.11842","created_at":"2026-07-05T02:01:21.830045+00:00"},{"alias_kind":"arxiv_version","alias_value":"2012.11842v1","created_at":"2026-07-05T02:01:21.830045+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2012.11842","created_at":"2026-07-05T02:01:21.830045+00:00"},{"alias_kind":"pith_short_12","alias_value":"CHE6NTLUBXN3","created_at":"2026-07-05T02:01:21.830045+00:00"},{"alias_kind":"pith_short_16","alias_value":"CHE6NTLUBXN3AMMX","created_at":"2026-07-05T02:01:21.830045+00:00"},{"alias_kind":"pith_short_8","alias_value":"CHE6NTLU","created_at":"2026-07-05T02:01:21.830045+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.24985","citing_title":"Retrieval-Augmented Personalization with Foundation Models for Wearable Stress Detection","ref_index":46,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CHE6NTLUBXN3AMMX24DJOAXGAK","json":"https://pith.science/pith/CHE6NTLUBXN3AMMX24DJOAXGAK.json","graph_json":"https://pith.science/api/pith-number/CHE6NTLUBXN3AMMX24DJOAXGAK/graph.json","events_json":"https://pith.science/api/pith-number/CHE6NTLUBXN3AMMX24DJOAXGAK/events.json","paper":"https://pith.science/paper/CHE6NTLU"},"agent_actions":{"view_html":"https://pith.science/pith/CHE6NTLUBXN3AMMX24DJOAXGAK","download_json":"https://pith.science/pith/CHE6NTLUBXN3AMMX24DJOAXGAK.json","view_paper":"https://pith.science/paper/CHE6NTLU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2012.11842&json=true","fetch_graph":"https://pith.science/api/pith-number/CHE6NTLUBXN3AMMX24DJOAXGAK/graph.json","fetch_events":"https://pith.science/api/pith-number/CHE6NTLUBXN3AMMX24DJOAXGAK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CHE6NTLUBXN3AMMX24DJOAXGAK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CHE6NTLUBXN3AMMX24DJOAXGAK/action/storage_attestation","attest_author":"https://pith.science/pith/CHE6NTLUBXN3AMMX24DJOAXGAK/action/author_attestation","sign_citation":"https://pith.science/pith/CHE6NTLUBXN3AMMX24DJOAXGAK/action/citation_signature","submit_replication":"https://pith.science/pith/CHE6NTLUBXN3AMMX24DJOAXGAK/action/replication_record"}},"created_at":"2026-07-05T02:01:21.830045+00:00","updated_at":"2026-07-05T02:01:21.830045+00:00"}