{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:IG4DOEC6OCZKSRG2NZU5S62MK4","short_pith_number":"pith:IG4DOEC6","schema_version":"1.0","canonical_sha256":"41b837105e70b2a944da6e69d97b4c571e509717ca3877339cebed20eeb8d965","source":{"kind":"arxiv","id":"2503.02614","version":2},"attestation_state":"computed","paper":{"title":"Personalized Generation In Large Model Era: A Survey","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Alireza Salemi, Fuli Feng, Hamed Zamani, Jinghao Zhang, Tat-Seng Chua, Wenjie Wang, Xiangnan He, Xinting Hu, Yiyan Xu","submitted_at":"2025-03-04T13:34:19Z","abstract_excerpt":"In the era of large models, content generation is gradually shifting to Personalized Generation (PGen), tailoring content to individual preferences and needs. This paper presents the first comprehensive survey on PGen, investigating existing research in this rapidly growing field. We conceptualize PGen from a unified perspective, systematically formalizing its key components, core objectives, and abstract workflows. Based on this unified perspective, we propose a multi-level taxonomy, offering an in-depth review of technical advancements, commonly used datasets, and evaluation metrics across m"},"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":"2503.02614","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2025-03-04T13:34:19Z","cross_cats_sorted":[],"title_canon_sha256":"f72b991ffc50643e7ec6120c8b34567e6b91e404908d5e70e31e2fa61018da5b","abstract_canon_sha256":"ba11e752a9d7cc426a25a88e9117e6b5ad352dc40d7ffd47d89a879e42c379e1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:13:22.037637Z","signature_b64":"drDOGEJsHhsyYCW/QWOpjaTe5maZ+HGwD4Jcj7mIHszyo/6abSj3ZP3T9QC5LhNWxKmoqYGN4533tsUkZIMhCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"41b837105e70b2a944da6e69d97b4c571e509717ca3877339cebed20eeb8d965","last_reissued_at":"2026-07-05T11:13:22.037165Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:13:22.037165Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Personalized Generation In Large Model Era: A Survey","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Alireza Salemi, Fuli Feng, Hamed Zamani, Jinghao Zhang, Tat-Seng Chua, Wenjie Wang, Xiangnan He, Xinting Hu, Yiyan Xu","submitted_at":"2025-03-04T13:34:19Z","abstract_excerpt":"In the era of large models, content generation is gradually shifting to Personalized Generation (PGen), tailoring content to individual preferences and needs. This paper presents the first comprehensive survey on PGen, investigating existing research in this rapidly growing field. We conceptualize PGen from a unified perspective, systematically formalizing its key components, core objectives, and abstract workflows. Based on this unified perspective, we propose a multi-level taxonomy, offering an in-depth review of technical advancements, commonly used datasets, and evaluation metrics across m"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.02614","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/2503.02614/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":"2503.02614","created_at":"2026-07-05T11:13:22.037224+00:00"},{"alias_kind":"arxiv_version","alias_value":"2503.02614v2","created_at":"2026-07-05T11:13:22.037224+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.02614","created_at":"2026-07-05T11:13:22.037224+00:00"},{"alias_kind":"pith_short_12","alias_value":"IG4DOEC6OCZK","created_at":"2026-07-05T11:13:22.037224+00:00"},{"alias_kind":"pith_short_16","alias_value":"IG4DOEC6OCZKSRG2","created_at":"2026-07-05T11:13:22.037224+00:00"},{"alias_kind":"pith_short_8","alias_value":"IG4DOEC6","created_at":"2026-07-05T11:13:22.037224+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.02300","citing_title":"Beyond Isolated Behaviors: Hierarchical User Modeling for LLM Personalization","ref_index":38,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/IG4DOEC6OCZKSRG2NZU5S62MK4","json":"https://pith.science/pith/IG4DOEC6OCZKSRG2NZU5S62MK4.json","graph_json":"https://pith.science/api/pith-number/IG4DOEC6OCZKSRG2NZU5S62MK4/graph.json","events_json":"https://pith.science/api/pith-number/IG4DOEC6OCZKSRG2NZU5S62MK4/events.json","paper":"https://pith.science/paper/IG4DOEC6"},"agent_actions":{"view_html":"https://pith.science/pith/IG4DOEC6OCZKSRG2NZU5S62MK4","download_json":"https://pith.science/pith/IG4DOEC6OCZKSRG2NZU5S62MK4.json","view_paper":"https://pith.science/paper/IG4DOEC6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2503.02614&json=true","fetch_graph":"https://pith.science/api/pith-number/IG4DOEC6OCZKSRG2NZU5S62MK4/graph.json","fetch_events":"https://pith.science/api/pith-number/IG4DOEC6OCZKSRG2NZU5S62MK4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IG4DOEC6OCZKSRG2NZU5S62MK4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IG4DOEC6OCZKSRG2NZU5S62MK4/action/storage_attestation","attest_author":"https://pith.science/pith/IG4DOEC6OCZKSRG2NZU5S62MK4/action/author_attestation","sign_citation":"https://pith.science/pith/IG4DOEC6OCZKSRG2NZU5S62MK4/action/citation_signature","submit_replication":"https://pith.science/pith/IG4DOEC6OCZKSRG2NZU5S62MK4/action/replication_record"}},"created_at":"2026-07-05T11:13:22.037224+00:00","updated_at":"2026-07-05T11:13:22.037224+00:00"}