{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:Y7SCUQADF3Y4IACYL7OCQQONJQ","short_pith_number":"pith:Y7SCUQAD","schema_version":"1.0","canonical_sha256":"c7e42a40032ef1c400585fdc2841cd4c293158cb4da35f6f51823d816b48d694","source":{"kind":"arxiv","id":"2506.01704","version":1},"attestation_state":"computed","paper":{"title":"Generate, Not Recommend: Personalized Multimodal Content Generation","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.AI","authors_text":"Chenyan Xiong, Jiongnan Liu, Ning Hu, Zhicheng Dou","submitted_at":"2025-06-02T14:10:08Z","abstract_excerpt":"To address the challenge of information overload from massive web contents, recommender systems are widely applied to retrieve and present personalized results for users. However, recommendation tasks are inherently constrained to filtering existing items and lack the ability to generate novel concepts, limiting their capacity to fully satisfy user demands and preferences. In this paper, we propose a new paradigm that goes beyond content filtering and selecting: directly generating personalized items in a multimodal form, such as images, tailored to individual users. To accomplish this, we lev"},"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":"2506.01704","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.AI","submitted_at":"2025-06-02T14:10:08Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"81fddfa5fb72875c5e072a51027c042028531195dbb186caf534124abec56c87","abstract_canon_sha256":"3ad0eb207fb2d1b387659419f2daa481ac7cc01a2d7d88f9a89595d4169b9984"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:15:01.638136Z","signature_b64":"72K6AfkBDINjVjXCmi4FdxM5bbZeIUMsm6SmIX6HitdOVg8J9kzDIUCDhYfIIcduHrVxBabE9DIE2IMIbXekCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c7e42a40032ef1c400585fdc2841cd4c293158cb4da35f6f51823d816b48d694","last_reissued_at":"2026-07-05T11:15:01.637671Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:15:01.637671Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Generate, Not Recommend: Personalized Multimodal Content Generation","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.AI","authors_text":"Chenyan Xiong, Jiongnan Liu, Ning Hu, Zhicheng Dou","submitted_at":"2025-06-02T14:10:08Z","abstract_excerpt":"To address the challenge of information overload from massive web contents, recommender systems are widely applied to retrieve and present personalized results for users. However, recommendation tasks are inherently constrained to filtering existing items and lack the ability to generate novel concepts, limiting their capacity to fully satisfy user demands and preferences. In this paper, we propose a new paradigm that goes beyond content filtering and selecting: directly generating personalized items in a multimodal form, such as images, tailored to individual users. To accomplish this, we lev"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.01704","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/2506.01704/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":"2506.01704","created_at":"2026-07-05T11:15:01.637733+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.01704v1","created_at":"2026-07-05T11:15:01.637733+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.01704","created_at":"2026-07-05T11:15:01.637733+00:00"},{"alias_kind":"pith_short_12","alias_value":"Y7SCUQADF3Y4","created_at":"2026-07-05T11:15:01.637733+00:00"},{"alias_kind":"pith_short_16","alias_value":"Y7SCUQADF3Y4IACY","created_at":"2026-07-05T11:15:01.637733+00:00"},{"alias_kind":"pith_short_8","alias_value":"Y7SCUQAD","created_at":"2026-07-05T11:15:01.637733+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2509.04330","citing_title":"Temporal Interest-Driven Multimodal Personalized Content Generation","ref_index":16,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/Y7SCUQADF3Y4IACYL7OCQQONJQ","json":"https://pith.science/pith/Y7SCUQADF3Y4IACYL7OCQQONJQ.json","graph_json":"https://pith.science/api/pith-number/Y7SCUQADF3Y4IACYL7OCQQONJQ/graph.json","events_json":"https://pith.science/api/pith-number/Y7SCUQADF3Y4IACYL7OCQQONJQ/events.json","paper":"https://pith.science/paper/Y7SCUQAD"},"agent_actions":{"view_html":"https://pith.science/pith/Y7SCUQADF3Y4IACYL7OCQQONJQ","download_json":"https://pith.science/pith/Y7SCUQADF3Y4IACYL7OCQQONJQ.json","view_paper":"https://pith.science/paper/Y7SCUQAD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.01704&json=true","fetch_graph":"https://pith.science/api/pith-number/Y7SCUQADF3Y4IACYL7OCQQONJQ/graph.json","fetch_events":"https://pith.science/api/pith-number/Y7SCUQADF3Y4IACYL7OCQQONJQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Y7SCUQADF3Y4IACYL7OCQQONJQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Y7SCUQADF3Y4IACYL7OCQQONJQ/action/storage_attestation","attest_author":"https://pith.science/pith/Y7SCUQADF3Y4IACYL7OCQQONJQ/action/author_attestation","sign_citation":"https://pith.science/pith/Y7SCUQADF3Y4IACYL7OCQQONJQ/action/citation_signature","submit_replication":"https://pith.science/pith/Y7SCUQADF3Y4IACYL7OCQQONJQ/action/replication_record"}},"created_at":"2026-07-05T11:15:01.637733+00:00","updated_at":"2026-07-05T11:15:01.637733+00:00"}