{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:7JZA2GFJO7WQSOENXJ6ODQCG6E","short_pith_number":"pith:7JZA2GFJ","schema_version":"1.0","canonical_sha256":"fa720d18a977ed09388dba7ce1c046f10b125e2d0eebee87e62ac2b14065b759","source":{"kind":"arxiv","id":"2501.01945","version":2},"attestation_state":"computed","paper":{"title":"Cold-Start Recommendation towards the Era of Large Language Models (LLMs): A Comprehensive Survey and Roadmap","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.IR","authors_text":"Aiwei Liu, Allen Lin, Fakhri Karray, Feiran Huang, Hao Chen, Henry Peng Zou, Irwin King, James Caverlee, Jiajun Bu, Jianling Wang, Liangwei Yang, Peilin Zhou, Philip S. Yu, Sheng Zhou, Weizhi Zhang, Yinghui Li, Yuanchen Bei, Yu Wang","submitted_at":"2025-01-03T18:51:18Z","abstract_excerpt":"Cold-start problem is one of the long-standing challenges in recommender systems, focusing on accurately modeling new or interaction-limited users or items to provide better recommendations. Due to the diversification of internet platforms and the exponential growth of users and items, the importance of cold-start recommendation (CSR) is becoming increasingly evident. At the same time, large language models (LLMs) have achieved tremendous success and possess strong capabilities in modeling user and item information, providing new potential for cold-start recommendations. However, the research "},"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":"2501.01945","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2025-01-03T18:51:18Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"52fc4103e72bf47d22d757cb6ee12602abe107e216164190c89b1cb5a1a9025f","abstract_canon_sha256":"56847df1f827f4f28b2dacd34b8f25c2ab3b4dbb27d17b3855bd59203b49fadb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:02:09.190372Z","signature_b64":"H9vmKwX/3J+VWAp/33n980p9eghAnf9+w61Hsx0Dd3YUivwCnjDlzMVmeCBxPnG81z0/rAaglLgcPDb5UYhtBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fa720d18a977ed09388dba7ce1c046f10b125e2d0eebee87e62ac2b14065b759","last_reissued_at":"2026-07-05T10:02:09.189904Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:02:09.189904Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Cold-Start Recommendation towards the Era of Large Language Models (LLMs): A Comprehensive Survey and Roadmap","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.IR","authors_text":"Aiwei Liu, Allen Lin, Fakhri Karray, Feiran Huang, Hao Chen, Henry Peng Zou, Irwin King, James Caverlee, Jiajun Bu, Jianling Wang, Liangwei Yang, Peilin Zhou, Philip S. Yu, Sheng Zhou, Weizhi Zhang, Yinghui Li, Yuanchen Bei, Yu Wang","submitted_at":"2025-01-03T18:51:18Z","abstract_excerpt":"Cold-start problem is one of the long-standing challenges in recommender systems, focusing on accurately modeling new or interaction-limited users or items to provide better recommendations. Due to the diversification of internet platforms and the exponential growth of users and items, the importance of cold-start recommendation (CSR) is becoming increasingly evident. At the same time, large language models (LLMs) have achieved tremendous success and possess strong capabilities in modeling user and item information, providing new potential for cold-start recommendations. However, the research "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.01945","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/2501.01945/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":"2501.01945","created_at":"2026-07-05T10:02:09.189958+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.01945v2","created_at":"2026-07-05T10:02:09.189958+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.01945","created_at":"2026-07-05T10:02:09.189958+00:00"},{"alias_kind":"pith_short_12","alias_value":"7JZA2GFJO7WQ","created_at":"2026-07-05T10:02:09.189958+00:00"},{"alias_kind":"pith_short_16","alias_value":"7JZA2GFJO7WQSOEN","created_at":"2026-07-05T10:02:09.189958+00:00"},{"alias_kind":"pith_short_8","alias_value":"7JZA2GFJ","created_at":"2026-07-05T10:02:09.189958+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":8,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.17788","citing_title":"Uncertainty-Calibrated Recommendations for Low-Active Users","ref_index":22,"is_internal_anchor":false},{"citing_arxiv_id":"2605.29141","citing_title":"Toward User Preference Alignment in LLM Recommendation via Explicit Context Feedback","ref_index":16,"is_internal_anchor":false},{"citing_arxiv_id":"2605.17788","citing_title":"Uncertainty-Calibrated Recommendations for Low-Active Users","ref_index":22,"is_internal_anchor":false},{"citing_arxiv_id":"2605.17994","citing_title":"Towards Sustainable Growth: A Multi-Value-Aware Retrieval Framework for E-Commerce Search","ref_index":39,"is_internal_anchor":false},{"citing_arxiv_id":"2506.19500","citing_title":"NaviAgent: Bilevel Planning on Tool Navigation Graph for Large-Scale Orchestration","ref_index":19,"is_internal_anchor":false},{"citing_arxiv_id":"2604.20858","citing_title":"Mixture of Sequence: Theme-Aware Mixture-of-Experts for Long-Sequence Recommendation","ref_index":161,"is_internal_anchor":false},{"citing_arxiv_id":"2604.07090","citing_title":"Leveraging Artist Catalogs for Cold-Start Music Recommendation","ref_index":78,"is_internal_anchor":false},{"citing_arxiv_id":"2604.12990","citing_title":"Sparse Contrastive Learning for Content-Based Cold Item Recommendation","ref_index":68,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/7JZA2GFJO7WQSOENXJ6ODQCG6E","json":"https://pith.science/pith/7JZA2GFJO7WQSOENXJ6ODQCG6E.json","graph_json":"https://pith.science/api/pith-number/7JZA2GFJO7WQSOENXJ6ODQCG6E/graph.json","events_json":"https://pith.science/api/pith-number/7JZA2GFJO7WQSOENXJ6ODQCG6E/events.json","paper":"https://pith.science/paper/7JZA2GFJ"},"agent_actions":{"view_html":"https://pith.science/pith/7JZA2GFJO7WQSOENXJ6ODQCG6E","download_json":"https://pith.science/pith/7JZA2GFJO7WQSOENXJ6ODQCG6E.json","view_paper":"https://pith.science/paper/7JZA2GFJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.01945&json=true","fetch_graph":"https://pith.science/api/pith-number/7JZA2GFJO7WQSOENXJ6ODQCG6E/graph.json","fetch_events":"https://pith.science/api/pith-number/7JZA2GFJO7WQSOENXJ6ODQCG6E/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7JZA2GFJO7WQSOENXJ6ODQCG6E/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7JZA2GFJO7WQSOENXJ6ODQCG6E/action/storage_attestation","attest_author":"https://pith.science/pith/7JZA2GFJO7WQSOENXJ6ODQCG6E/action/author_attestation","sign_citation":"https://pith.science/pith/7JZA2GFJO7WQSOENXJ6ODQCG6E/action/citation_signature","submit_replication":"https://pith.science/pith/7JZA2GFJO7WQSOENXJ6ODQCG6E/action/replication_record"}},"created_at":"2026-07-05T10:02:09.189958+00:00","updated_at":"2026-07-05T10:02:09.189958+00:00"}