{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:KZ3XB3GL4NVMCAEMV56LLI7C52","short_pith_number":"pith:KZ3XB3GL","schema_version":"1.0","canonical_sha256":"567770eccbe36ac1008caf7cb5a3e2eeb667183d802469794c401f3342de2888","source":{"kind":"arxiv","id":"2204.01390","version":2},"attestation_state":"computed","paper":{"title":"A Comprehensive Survey on Automated Machine Learning for Recommendations","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.IR","authors_text":"Bo Chen, Huifeng Guo, Ruiming Tang, Wenqi Fan, Xiangyu Zhao, Yejing Wang","submitted_at":"2022-04-04T11:17:43Z","abstract_excerpt":"Deep recommender systems (DRS) are critical for current commercial online service providers, which address the issue of information overload by recommending items that are tailored to the user's interests and preferences. They have unprecedented feature representations effectiveness and the capacity of modeling the non-linear relationships between users and items. Despite their advancements, DRS models, like other deep learning models, employ sophisticated neural network architectures and other vital components that are typically designed and tuned by human experts. This article will give a co"},"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":"2204.01390","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.IR","submitted_at":"2022-04-04T11:17:43Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"b1531586439d0c579f151f8946aae9cd4ce0419dfc84c73c12d102728f6122ee","abstract_canon_sha256":"aeb1125f2a473e8c80af8527b2691bbcb5747164cfdcc7c57dbdaced0170cb61"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:42:24.875746Z","signature_b64":"TtgaplcFSCTSrs+gaN5AjZ1ZHQBWqVMZ6is7g6+xJwcUpqWOBeCZNoHOr388qlpzJTltMJ6j5i4EUDZY2SEuDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"567770eccbe36ac1008caf7cb5a3e2eeb667183d802469794c401f3342de2888","last_reissued_at":"2026-07-05T05:42:24.875253Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:42:24.875253Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Comprehensive Survey on Automated Machine Learning for Recommendations","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.IR","authors_text":"Bo Chen, Huifeng Guo, Ruiming Tang, Wenqi Fan, Xiangyu Zhao, Yejing Wang","submitted_at":"2022-04-04T11:17:43Z","abstract_excerpt":"Deep recommender systems (DRS) are critical for current commercial online service providers, which address the issue of information overload by recommending items that are tailored to the user's interests and preferences. They have unprecedented feature representations effectiveness and the capacity of modeling the non-linear relationships between users and items. Despite their advancements, DRS models, like other deep learning models, employ sophisticated neural network architectures and other vital components that are typically designed and tuned by human experts. This article will give a co"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2204.01390","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/2204.01390/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":"2204.01390","created_at":"2026-07-05T05:42:24.875310+00:00"},{"alias_kind":"arxiv_version","alias_value":"2204.01390v2","created_at":"2026-07-05T05:42:24.875310+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2204.01390","created_at":"2026-07-05T05:42:24.875310+00:00"},{"alias_kind":"pith_short_12","alias_value":"KZ3XB3GL4NVM","created_at":"2026-07-05T05:42:24.875310+00:00"},{"alias_kind":"pith_short_16","alias_value":"KZ3XB3GL4NVMCAEM","created_at":"2026-07-05T05:42:24.875310+00:00"},{"alias_kind":"pith_short_8","alias_value":"KZ3XB3GL","created_at":"2026-07-05T05:42:24.875310+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.04671","citing_title":"DANCE: Resource-Efficient Neural Architecture Search with Data-Aware and Continuous Adaptation","ref_index":2019,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KZ3XB3GL4NVMCAEMV56LLI7C52","json":"https://pith.science/pith/KZ3XB3GL4NVMCAEMV56LLI7C52.json","graph_json":"https://pith.science/api/pith-number/KZ3XB3GL4NVMCAEMV56LLI7C52/graph.json","events_json":"https://pith.science/api/pith-number/KZ3XB3GL4NVMCAEMV56LLI7C52/events.json","paper":"https://pith.science/paper/KZ3XB3GL"},"agent_actions":{"view_html":"https://pith.science/pith/KZ3XB3GL4NVMCAEMV56LLI7C52","download_json":"https://pith.science/pith/KZ3XB3GL4NVMCAEMV56LLI7C52.json","view_paper":"https://pith.science/paper/KZ3XB3GL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2204.01390&json=true","fetch_graph":"https://pith.science/api/pith-number/KZ3XB3GL4NVMCAEMV56LLI7C52/graph.json","fetch_events":"https://pith.science/api/pith-number/KZ3XB3GL4NVMCAEMV56LLI7C52/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KZ3XB3GL4NVMCAEMV56LLI7C52/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KZ3XB3GL4NVMCAEMV56LLI7C52/action/storage_attestation","attest_author":"https://pith.science/pith/KZ3XB3GL4NVMCAEMV56LLI7C52/action/author_attestation","sign_citation":"https://pith.science/pith/KZ3XB3GL4NVMCAEMV56LLI7C52/action/citation_signature","submit_replication":"https://pith.science/pith/KZ3XB3GL4NVMCAEMV56LLI7C52/action/replication_record"}},"created_at":"2026-07-05T05:42:24.875310+00:00","updated_at":"2026-07-05T05:42:24.875310+00:00"}