{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:D6YXRVJFY6PPQJHPS3BEBLNJ3N","short_pith_number":"pith:D6YXRVJF","canonical_record":{"source":{"id":"2507.02000","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2025-07-01T11:39:42Z","cross_cats_sorted":["cs.CL","cs.MM"],"title_canon_sha256":"d33d85c6c217f2d7e5a7328a32a1e388d2feebc99e1fbb9190379ba750b3d573","abstract_canon_sha256":"279beba05c6e39e4a59161b6d4cbf47dc2dbd47953045ae3e7b71177f19f4fd1"},"schema_version":"1.0"},"canonical_sha256":"1fb178d525c79ef824ef96c240ada9db42c7f73679325a9244f5eaffeb81d9da","source":{"kind":"arxiv","id":"2507.02000","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.02000","created_at":"2026-07-05T11:31:10Z"},{"alias_kind":"arxiv_version","alias_value":"2507.02000v1","created_at":"2026-07-05T11:31:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.02000","created_at":"2026-07-05T11:31:10Z"},{"alias_kind":"pith_short_12","alias_value":"D6YXRVJFY6PP","created_at":"2026-07-05T11:31:10Z"},{"alias_kind":"pith_short_16","alias_value":"D6YXRVJFY6PPQJHP","created_at":"2026-07-05T11:31:10Z"},{"alias_kind":"pith_short_8","alias_value":"D6YXRVJF","created_at":"2026-07-05T11:31:10Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:D6YXRVJFY6PPQJHPS3BEBLNJ3N","target":"record","payload":{"canonical_record":{"source":{"id":"2507.02000","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2025-07-01T11:39:42Z","cross_cats_sorted":["cs.CL","cs.MM"],"title_canon_sha256":"d33d85c6c217f2d7e5a7328a32a1e388d2feebc99e1fbb9190379ba750b3d573","abstract_canon_sha256":"279beba05c6e39e4a59161b6d4cbf47dc2dbd47953045ae3e7b71177f19f4fd1"},"schema_version":"1.0"},"canonical_sha256":"1fb178d525c79ef824ef96c240ada9db42c7f73679325a9244f5eaffeb81d9da","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:31:10.248989Z","signature_b64":"YodsHw7buiJRxJ5+l/WiIQKs4yy7sY5e1skEylwEf3g08DG2jm+PIT6keKcJsAybxz+uw5JZiyfDTfQZ+WhXDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1fb178d525c79ef824ef96c240ada9db42c7f73679325a9244f5eaffeb81d9da","last_reissued_at":"2026-07-05T11:31:10.248580Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:31:10.248580Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2507.02000","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:31:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gIja9u/zKquV23KA1cZGLxjYnIHrBUor1q4ngtIudF0Bq+uiQa9ORx3CsseQnX70ojoI3xQVCP/tZs8FLNerCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T06:34:13.153263Z"},"content_sha256":"2d2a513c86a3dad4d2c55cae2cc0e24fcedc0bbdc0288260a0de54ce21d03308","schema_version":"1.0","event_id":"sha256:2d2a513c86a3dad4d2c55cae2cc0e24fcedc0bbdc0288260a0de54ce21d03308"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:D6YXRVJFY6PPQJHPS3BEBLNJ3N","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Why Multi-Interest Fairness Matters: Hypergraph Contrastive Multi-Interest Learning for Fair Conversational Recommender System","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.MM"],"primary_cat":"cs.IR","authors_text":"Guohua Wang, Kwok-Yan Lam, Liang Lin, Yongsen Zheng, Ziyao Liu, Zongxuan Xie","submitted_at":"2025-07-01T11:39:42Z","abstract_excerpt":"Unfairness is a well-known challenge in Recommender Systems (RSs), often resulting in biased outcomes that disadvantage users or items based on attributes such as gender, race, age, or popularity. Although some approaches have started to improve fairness recommendation in offline or static contexts, the issue of unfairness often exacerbates over time, leading to significant problems like the Matthew effect, filter bubbles, and echo chambers. To address these challenges, we proposed a novel framework, Hypergraph Contrastive Multi-Interest Learning for Fair Conversational Recommender System (HyF"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.02000","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/2507.02000/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":"2026-07-05T11:31:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6O4qnwZq/lvlEkrrM24JqvY6sT1ieTQuusA7VXRBvPmXJTgoAH4J8D1KuEwALpaJrvil1b53QPfUEAfBfAXoDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T06:34:13.153754Z"},"content_sha256":"463b0bbd1e98b958bc6f1a07e0f2f53a099dda0504b86f27424cf1d864078da0","schema_version":"1.0","event_id":"sha256:463b0bbd1e98b958bc6f1a07e0f2f53a099dda0504b86f27424cf1d864078da0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/D6YXRVJFY6PPQJHPS3BEBLNJ3N/bundle.json","state_url":"https://pith.science/pith/D6YXRVJFY6PPQJHPS3BEBLNJ3N/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/D6YXRVJFY6PPQJHPS3BEBLNJ3N/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":"2026-08-09T06:34:13Z","links":{"resolver":"https://pith.science/pith/D6YXRVJFY6PPQJHPS3BEBLNJ3N","bundle":"https://pith.science/pith/D6YXRVJFY6PPQJHPS3BEBLNJ3N/bundle.json","state":"https://pith.science/pith/D6YXRVJFY6PPQJHPS3BEBLNJ3N/state.json","well_known_bundle":"https://pith.science/.well-known/pith/D6YXRVJFY6PPQJHPS3BEBLNJ3N/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:D6YXRVJFY6PPQJHPS3BEBLNJ3N","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":"279beba05c6e39e4a59161b6d4cbf47dc2dbd47953045ae3e7b71177f19f4fd1","cross_cats_sorted":["cs.CL","cs.MM"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2025-07-01T11:39:42Z","title_canon_sha256":"d33d85c6c217f2d7e5a7328a32a1e388d2feebc99e1fbb9190379ba750b3d573"},"schema_version":"1.0","source":{"id":"2507.02000","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.02000","created_at":"2026-07-05T11:31:10Z"},{"alias_kind":"arxiv_version","alias_value":"2507.02000v1","created_at":"2026-07-05T11:31:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.02000","created_at":"2026-07-05T11:31:10Z"},{"alias_kind":"pith_short_12","alias_value":"D6YXRVJFY6PP","created_at":"2026-07-05T11:31:10Z"},{"alias_kind":"pith_short_16","alias_value":"D6YXRVJFY6PPQJHP","created_at":"2026-07-05T11:31:10Z"},{"alias_kind":"pith_short_8","alias_value":"D6YXRVJF","created_at":"2026-07-05T11:31:10Z"}],"graph_snapshots":[{"event_id":"sha256:463b0bbd1e98b958bc6f1a07e0f2f53a099dda0504b86f27424cf1d864078da0","target":"graph","created_at":"2026-07-05T11:31:10Z","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/2507.02000/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Unfairness is a well-known challenge in Recommender Systems (RSs), often resulting in biased outcomes that disadvantage users or items based on attributes such as gender, race, age, or popularity. Although some approaches have started to improve fairness recommendation in offline or static contexts, the issue of unfairness often exacerbates over time, leading to significant problems like the Matthew effect, filter bubbles, and echo chambers. To address these challenges, we proposed a novel framework, Hypergraph Contrastive Multi-Interest Learning for Fair Conversational Recommender System (HyF","authors_text":"Guohua Wang, Kwok-Yan Lam, Liang Lin, Yongsen Zheng, Ziyao Liu, Zongxuan Xie","cross_cats":["cs.CL","cs.MM"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2025-07-01T11:39:42Z","title":"Why Multi-Interest Fairness Matters: Hypergraph Contrastive Multi-Interest Learning for Fair Conversational Recommender System"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.02000","kind":"arxiv","version":1},"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":"sha256:2d2a513c86a3dad4d2c55cae2cc0e24fcedc0bbdc0288260a0de54ce21d03308","target":"record","created_at":"2026-07-05T11:31:10Z","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":"279beba05c6e39e4a59161b6d4cbf47dc2dbd47953045ae3e7b71177f19f4fd1","cross_cats_sorted":["cs.CL","cs.MM"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2025-07-01T11:39:42Z","title_canon_sha256":"d33d85c6c217f2d7e5a7328a32a1e388d2feebc99e1fbb9190379ba750b3d573"},"schema_version":"1.0","source":{"id":"2507.02000","kind":"arxiv","version":1}},"canonical_sha256":"1fb178d525c79ef824ef96c240ada9db42c7f73679325a9244f5eaffeb81d9da","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1fb178d525c79ef824ef96c240ada9db42c7f73679325a9244f5eaffeb81d9da","first_computed_at":"2026-07-05T11:31:10.248580Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:31:10.248580Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"YodsHw7buiJRxJ5+l/WiIQKs4yy7sY5e1skEylwEf3g08DG2jm+PIT6keKcJsAybxz+uw5JZiyfDTfQZ+WhXDw==","signature_status":"signed_v1","signed_at":"2026-07-05T11:31:10.248989Z","signed_message":"canonical_sha256_bytes"},"source_id":"2507.02000","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2d2a513c86a3dad4d2c55cae2cc0e24fcedc0bbdc0288260a0de54ce21d03308","sha256:463b0bbd1e98b958bc6f1a07e0f2f53a099dda0504b86f27424cf1d864078da0"],"state_sha256":"d5b682eea810095a97572fc86a4a042480b82630cd9a6a5c0dbf78d7fc33fc6b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jM0sFlTaxkvRuEi6GHYbbXA1G4XccDLRl/KOnSe2hvec73wFbDxgXfUOroo7Awck9R+oTZcVR0wBZkbBUzl/BA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T06:34:13.157451Z","bundle_sha256":"24a3017c12024614dd969579b8d474e9f7de37a7f3a4acd0da64ee25f1c5de48"}}