{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:NJST6CGY5BPLCBYJGLFRGWZMEK","short_pith_number":"pith:NJST6CGY","canonical_record":{"source":{"id":"2410.13117","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2024-10-17T01:02:04Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"d959f9f4993088423a8289b0c04c80d6b9d93188d383e7b5d9f0bc93418764fe","abstract_canon_sha256":"681710c2fc32fb8cfdd91ed4ccc0a6eb9eae64aef5d1028500b090225a92d25c"},"schema_version":"1.0"},"canonical_sha256":"6a653f08d8e85eb1070932cb135b2c228dfc03d4cb8c789d71d52af2a51681e3","source":{"kind":"arxiv","id":"2410.13117","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.13117","created_at":"2026-07-05T10:51:26Z"},{"alias_kind":"arxiv_version","alias_value":"2410.13117v2","created_at":"2026-07-05T10:51:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.13117","created_at":"2026-07-05T10:51:26Z"},{"alias_kind":"pith_short_12","alias_value":"NJST6CGY5BPL","created_at":"2026-07-05T10:51:26Z"},{"alias_kind":"pith_short_16","alias_value":"NJST6CGY5BPLCBYJ","created_at":"2026-07-05T10:51:26Z"},{"alias_kind":"pith_short_8","alias_value":"NJST6CGY","created_at":"2026-07-05T10:51:26Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:NJST6CGY5BPLCBYJGLFRGWZMEK","target":"record","payload":{"canonical_record":{"source":{"id":"2410.13117","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2024-10-17T01:02:04Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"d959f9f4993088423a8289b0c04c80d6b9d93188d383e7b5d9f0bc93418764fe","abstract_canon_sha256":"681710c2fc32fb8cfdd91ed4ccc0a6eb9eae64aef5d1028500b090225a92d25c"},"schema_version":"1.0"},"canonical_sha256":"6a653f08d8e85eb1070932cb135b2c228dfc03d4cb8c789d71d52af2a51681e3","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:51:26.712665Z","signature_b64":"B5W0OcsSz/OAR926hvo8J3AJyAVzCpw5RdlJV5ANF57/DD0OtFlllYugey4cBHaNZDflLPjca7o6c3dFZyRnBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6a653f08d8e85eb1070932cb135b2c228dfc03d4cb8c789d71d52af2a51681e3","last_reissued_at":"2026-07-05T10:51:26.712187Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:51:26.712187Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2410.13117","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:51:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/BIZ3wEfXXiW7RBNWFruqHOdbw/VlXonUsYU51fFpCqKA0COGmwv+rckjorSvTMM823QDUCy1PM2WUQKJqO/Dg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T22:11:58.702091Z"},"content_sha256":"0f3517258d4647f2395d2adc8a7ff954e3de17d9fbe8a5c00f107e17c39e4b49","schema_version":"1.0","event_id":"sha256:0f3517258d4647f2395d2adc8a7ff954e3de17d9fbe8a5c00f107e17c39e4b49"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:NJST6CGY5BPLCBYJGLFRGWZMEK","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Preference Diffusion for Recommendation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.IR","authors_text":"An Zhang, Guoqing Hu, Hong Qian, Shuo Liu, Tat-Seng Chua","submitted_at":"2024-10-17T01:02:04Z","abstract_excerpt":"Recommender systems predict personalized item rankings based on user preference distributions derived from historical behavior data. Recently, diffusion models (DMs) have gained attention in recommendation for their ability to model complex distributions, yet current DM-based recommenders often rely on traditional objectives like mean squared error (MSE) or recommendation objectives, which are not optimized for personalized ranking tasks or fail to fully leverage DM's generative potential. To address this, we propose PreferDiff, a tailored optimization objective for DM-based recommenders. Pref"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.13117","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/2410.13117/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:51:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"uWvm7OhAC2EyjVysYhb06xe75khnCrLLfUf/WuH1o/huZD9k/zI4aCrqxlsc9Ft3mJrqAZZE7YAiCPCFlGi+Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T22:11:58.702468Z"},"content_sha256":"0bbcdcc50380b2ddac6d6b9ba793c10fb4e7fd89102e9b954ab894ac2fb56f9d","schema_version":"1.0","event_id":"sha256:0bbcdcc50380b2ddac6d6b9ba793c10fb4e7fd89102e9b954ab894ac2fb56f9d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/NJST6CGY5BPLCBYJGLFRGWZMEK/bundle.json","state_url":"https://pith.science/pith/NJST6CGY5BPLCBYJGLFRGWZMEK/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/NJST6CGY5BPLCBYJGLFRGWZMEK/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-11T22:11:58Z","links":{"resolver":"https://pith.science/pith/NJST6CGY5BPLCBYJGLFRGWZMEK","bundle":"https://pith.science/pith/NJST6CGY5BPLCBYJGLFRGWZMEK/bundle.json","state":"https://pith.science/pith/NJST6CGY5BPLCBYJGLFRGWZMEK/state.json","well_known_bundle":"https://pith.science/.well-known/pith/NJST6CGY5BPLCBYJGLFRGWZMEK/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:NJST6CGY5BPLCBYJGLFRGWZMEK","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"681710c2fc32fb8cfdd91ed4ccc0a6eb9eae64aef5d1028500b090225a92d25c","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2024-10-17T01:02:04Z","title_canon_sha256":"d959f9f4993088423a8289b0c04c80d6b9d93188d383e7b5d9f0bc93418764fe"},"schema_version":"1.0","source":{"id":"2410.13117","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.13117","created_at":"2026-07-05T10:51:26Z"},{"alias_kind":"arxiv_version","alias_value":"2410.13117v2","created_at":"2026-07-05T10:51:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.13117","created_at":"2026-07-05T10:51:26Z"},{"alias_kind":"pith_short_12","alias_value":"NJST6CGY5BPL","created_at":"2026-07-05T10:51:26Z"},{"alias_kind":"pith_short_16","alias_value":"NJST6CGY5BPLCBYJ","created_at":"2026-07-05T10:51:26Z"},{"alias_kind":"pith_short_8","alias_value":"NJST6CGY","created_at":"2026-07-05T10:51:26Z"}],"graph_snapshots":[{"event_id":"sha256:0bbcdcc50380b2ddac6d6b9ba793c10fb4e7fd89102e9b954ab894ac2fb56f9d","target":"graph","created_at":"2026-07-05T10:51:26Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2410.13117/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recommender systems predict personalized item rankings based on user preference distributions derived from historical behavior data. Recently, diffusion models (DMs) have gained attention in recommendation for their ability to model complex distributions, yet current DM-based recommenders often rely on traditional objectives like mean squared error (MSE) or recommendation objectives, which are not optimized for personalized ranking tasks or fail to fully leverage DM's generative potential. To address this, we propose PreferDiff, a tailored optimization objective for DM-based recommenders. Pref","authors_text":"An Zhang, Guoqing Hu, Hong Qian, Shuo Liu, Tat-Seng Chua","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2024-10-17T01:02:04Z","title":"Preference Diffusion for Recommendation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.13117","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:0f3517258d4647f2395d2adc8a7ff954e3de17d9fbe8a5c00f107e17c39e4b49","target":"record","created_at":"2026-07-05T10:51:26Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"681710c2fc32fb8cfdd91ed4ccc0a6eb9eae64aef5d1028500b090225a92d25c","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2024-10-17T01:02:04Z","title_canon_sha256":"d959f9f4993088423a8289b0c04c80d6b9d93188d383e7b5d9f0bc93418764fe"},"schema_version":"1.0","source":{"id":"2410.13117","kind":"arxiv","version":2}},"canonical_sha256":"6a653f08d8e85eb1070932cb135b2c228dfc03d4cb8c789d71d52af2a51681e3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6a653f08d8e85eb1070932cb135b2c228dfc03d4cb8c789d71d52af2a51681e3","first_computed_at":"2026-07-05T10:51:26.712187Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:51:26.712187Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"B5W0OcsSz/OAR926hvo8J3AJyAVzCpw5RdlJV5ANF57/DD0OtFlllYugey4cBHaNZDflLPjca7o6c3dFZyRnBw==","signature_status":"signed_v1","signed_at":"2026-07-05T10:51:26.712665Z","signed_message":"canonical_sha256_bytes"},"source_id":"2410.13117","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0f3517258d4647f2395d2adc8a7ff954e3de17d9fbe8a5c00f107e17c39e4b49","sha256:0bbcdcc50380b2ddac6d6b9ba793c10fb4e7fd89102e9b954ab894ac2fb56f9d"],"state_sha256":"25661e9991f0244bd00514bc0b23b8d872eb8263e43ab4b1de7e9d1461ef9265"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ybANvKd622Mddx7fKzKkBWbSXavSv1t3h+8qvEbNcf0QibylaL8DfRUz+Hqdl5c2jLNX8IVeFjxOtT36YQCPBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-11T22:11:58.704881Z","bundle_sha256":"2cbaf149860c74b81422071493204998aff80dae65cd180df89d756fcd09b02d"}}