{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:TGNTNI45J5NDQYA7CB23YAW7A6","short_pith_number":"pith:TGNTNI45","schema_version":"1.0","canonical_sha256":"999b36a39d4f5a38601f1075bc02df07b342be33673d08619b3b1daa00f26219","source":{"kind":"arxiv","id":"2203.10354","version":1},"attestation_state":"computed","paper":{"title":"Meta-Learning for Online Update of Recommender Systems","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.IR","authors_text":"Dongmin Park, Hwanjun Song, Jae-Gil Lee, Kijung Shin, Minseok Kim, Yooju Shin","submitted_at":"2022-03-19T16:27:30Z","abstract_excerpt":"Online recommender systems should be always aligned with users' current interest to accurately suggest items that each user would like. Since user interest usually evolves over time, the update strategy should be flexible to quickly catch users' current interest from continuously generated new user-item interactions. Existing update strategies focus either on the importance of each user-item interaction or the learning rate for each recommender parameter, but such one-directional flexibility is insufficient to adapt to varying relationships between interactions and parameters. In this paper, w"},"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":"2203.10354","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2022-03-19T16:27:30Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"d8b323a8499e32611a109a16f1b1036b32278ffe983f68ecfd0dc075bb6edd82","abstract_canon_sha256":"c0128c216064c19a1cad40d9a3d8c82beadf3be42bd98d59106ee265f9c71696"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:06:52.112611Z","signature_b64":"KhNJa/9o5rO1bWXXUk2TAFXvRqPmUMaypbl7MDxK9D5zulg8/6jv+FHi+ZPYA71wTK+x2yIJ79JAt2muGqqsAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"999b36a39d4f5a38601f1075bc02df07b342be33673d08619b3b1daa00f26219","last_reissued_at":"2026-07-05T04:06:52.112171Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:06:52.112171Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Meta-Learning for Online Update of Recommender Systems","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.IR","authors_text":"Dongmin Park, Hwanjun Song, Jae-Gil Lee, Kijung Shin, Minseok Kim, Yooju Shin","submitted_at":"2022-03-19T16:27:30Z","abstract_excerpt":"Online recommender systems should be always aligned with users' current interest to accurately suggest items that each user would like. Since user interest usually evolves over time, the update strategy should be flexible to quickly catch users' current interest from continuously generated new user-item interactions. Existing update strategies focus either on the importance of each user-item interaction or the learning rate for each recommender parameter, but such one-directional flexibility is insufficient to adapt to varying relationships between interactions and parameters. In this paper, w"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.10354","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/2203.10354/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":"2203.10354","created_at":"2026-07-05T04:06:52.112230+00:00"},{"alias_kind":"arxiv_version","alias_value":"2203.10354v1","created_at":"2026-07-05T04:06:52.112230+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.10354","created_at":"2026-07-05T04:06:52.112230+00:00"},{"alias_kind":"pith_short_12","alias_value":"TGNTNI45J5ND","created_at":"2026-07-05T04:06:52.112230+00:00"},{"alias_kind":"pith_short_16","alias_value":"TGNTNI45J5NDQYA7","created_at":"2026-07-05T04:06:52.112230+00:00"},{"alias_kind":"pith_short_8","alias_value":"TGNTNI45","created_at":"2026-07-05T04:06:52.112230+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2411.11225","citing_title":"Online Item Cold-Start Recommendation with Popularity-Aware Meta-Learning","ref_index":15,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/TGNTNI45J5NDQYA7CB23YAW7A6","json":"https://pith.science/pith/TGNTNI45J5NDQYA7CB23YAW7A6.json","graph_json":"https://pith.science/api/pith-number/TGNTNI45J5NDQYA7CB23YAW7A6/graph.json","events_json":"https://pith.science/api/pith-number/TGNTNI45J5NDQYA7CB23YAW7A6/events.json","paper":"https://pith.science/paper/TGNTNI45"},"agent_actions":{"view_html":"https://pith.science/pith/TGNTNI45J5NDQYA7CB23YAW7A6","download_json":"https://pith.science/pith/TGNTNI45J5NDQYA7CB23YAW7A6.json","view_paper":"https://pith.science/paper/TGNTNI45","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2203.10354&json=true","fetch_graph":"https://pith.science/api/pith-number/TGNTNI45J5NDQYA7CB23YAW7A6/graph.json","fetch_events":"https://pith.science/api/pith-number/TGNTNI45J5NDQYA7CB23YAW7A6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TGNTNI45J5NDQYA7CB23YAW7A6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TGNTNI45J5NDQYA7CB23YAW7A6/action/storage_attestation","attest_author":"https://pith.science/pith/TGNTNI45J5NDQYA7CB23YAW7A6/action/author_attestation","sign_citation":"https://pith.science/pith/TGNTNI45J5NDQYA7CB23YAW7A6/action/citation_signature","submit_replication":"https://pith.science/pith/TGNTNI45J5NDQYA7CB23YAW7A6/action/replication_record"}},"created_at":"2026-07-05T04:06:52.112230+00:00","updated_at":"2026-07-05T04:06:52.112230+00:00"}