{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:SXLQVYXJYK6F327OADMVDDJZL5","short_pith_number":"pith:SXLQVYXJ","schema_version":"1.0","canonical_sha256":"95d70ae2e9c2bc5debee00d9518d395f4c7ab010a096afd6e5315237af409f35","source":{"kind":"arxiv","id":"2412.00714","version":1},"attestation_state":"computed","paper":{"title":"Scaling New Frontiers: Insights into Large Recommendation Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Defu Lian, Enhong Chen, Hao Wang, Huifeng Guo, Jin Yao Chin, Kai Cheng, Kefan Wang, Kenan Song, Luankang Zhang, Qiushi Pan, Ruiming Tang, Tingjia Shen, Wanqi Xue, Wei Guo, Wenjia Xie, Yi Quan Lee, Yong Liu, Yuyang Ye, Zhongzhou Liu","submitted_at":"2024-12-01T07:27:20Z","abstract_excerpt":"Recommendation systems are essential for filtering data and retrieving relevant information across various applications. Recent advancements have seen these systems incorporate increasingly large embedding tables, scaling up to tens of terabytes for industrial use. However, the expansion of network parameters in traditional recommendation models has plateaued at tens of millions, limiting further benefits from increased embedding parameters. Inspired by the success of large language models (LLMs), a new approach has emerged that scales network parameters using innovative structures, enabling c"},"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":"2412.00714","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2024-12-01T07:27:20Z","cross_cats_sorted":[],"title_canon_sha256":"15d2c4496b4039825fb688ff4bce83c3b7eead071254c16a3c037b6f84077cc7","abstract_canon_sha256":"5d5f579d0dd5193f1410b6f8a9049033115a1ad20155fac399050e375a2cad46"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:42:45.748424Z","signature_b64":"Faupt/wS5aI0dYlh8iGuWiUtNSGivx/N+hgzvU3tWtTz9SIu4iujkkmqBsbgbeV89p8xNq54HonF0Eq8fUdDBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"95d70ae2e9c2bc5debee00d9518d395f4c7ab010a096afd6e5315237af409f35","last_reissued_at":"2026-07-05T09:42:45.747908Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:42:45.747908Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Scaling New Frontiers: Insights into Large Recommendation Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Defu Lian, Enhong Chen, Hao Wang, Huifeng Guo, Jin Yao Chin, Kai Cheng, Kefan Wang, Kenan Song, Luankang Zhang, Qiushi Pan, Ruiming Tang, Tingjia Shen, Wanqi Xue, Wei Guo, Wenjia Xie, Yi Quan Lee, Yong Liu, Yuyang Ye, Zhongzhou Liu","submitted_at":"2024-12-01T07:27:20Z","abstract_excerpt":"Recommendation systems are essential for filtering data and retrieving relevant information across various applications. Recent advancements have seen these systems incorporate increasingly large embedding tables, scaling up to tens of terabytes for industrial use. However, the expansion of network parameters in traditional recommendation models has plateaued at tens of millions, limiting further benefits from increased embedding parameters. Inspired by the success of large language models (LLMs), a new approach has emerged that scales network parameters using innovative structures, enabling c"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.00714","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/2412.00714/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":"2412.00714","created_at":"2026-07-05T09:42:45.747963+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.00714v1","created_at":"2026-07-05T09:42:45.747963+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.00714","created_at":"2026-07-05T09:42:45.747963+00:00"},{"alias_kind":"pith_short_12","alias_value":"SXLQVYXJYK6F","created_at":"2026-07-05T09:42:45.747963+00:00"},{"alias_kind":"pith_short_16","alias_value":"SXLQVYXJYK6F327O","created_at":"2026-07-05T09:42:45.747963+00:00"},{"alias_kind":"pith_short_8","alias_value":"SXLQVYXJ","created_at":"2026-07-05T09:42:45.747963+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.21911","citing_title":"The Pitfall of Scaling Up: Uncovering and Mitigating Popularity Bias Amplification in Scaling Transformer-based Recommenders","ref_index":26,"is_internal_anchor":false},{"citing_arxiv_id":"2605.23191","citing_title":"Expand More, Shrink Less: Shaping Effective-Rank Dynamics for Dense Scaling in Recommendation","ref_index":8,"is_internal_anchor":false},{"citing_arxiv_id":"2509.09682","citing_title":"Faster and Memory-Efficient Training of Sequential Recommendation Models for Large Catalogs","ref_index":9,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SXLQVYXJYK6F327OADMVDDJZL5","json":"https://pith.science/pith/SXLQVYXJYK6F327OADMVDDJZL5.json","graph_json":"https://pith.science/api/pith-number/SXLQVYXJYK6F327OADMVDDJZL5/graph.json","events_json":"https://pith.science/api/pith-number/SXLQVYXJYK6F327OADMVDDJZL5/events.json","paper":"https://pith.science/paper/SXLQVYXJ"},"agent_actions":{"view_html":"https://pith.science/pith/SXLQVYXJYK6F327OADMVDDJZL5","download_json":"https://pith.science/pith/SXLQVYXJYK6F327OADMVDDJZL5.json","view_paper":"https://pith.science/paper/SXLQVYXJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.00714&json=true","fetch_graph":"https://pith.science/api/pith-number/SXLQVYXJYK6F327OADMVDDJZL5/graph.json","fetch_events":"https://pith.science/api/pith-number/SXLQVYXJYK6F327OADMVDDJZL5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SXLQVYXJYK6F327OADMVDDJZL5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SXLQVYXJYK6F327OADMVDDJZL5/action/storage_attestation","attest_author":"https://pith.science/pith/SXLQVYXJYK6F327OADMVDDJZL5/action/author_attestation","sign_citation":"https://pith.science/pith/SXLQVYXJYK6F327OADMVDDJZL5/action/citation_signature","submit_replication":"https://pith.science/pith/SXLQVYXJYK6F327OADMVDDJZL5/action/replication_record"}},"created_at":"2026-07-05T09:42:45.747963+00:00","updated_at":"2026-07-05T09:42:45.747963+00:00"}