{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:KPK4Q4PPAHS6BKCGBFORDG46GH","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":"02f220d4d40ec2d16afe8f63e3b87a4e342b2a4dacffed20b386ea575840f8d3","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2023-10-31T00:12:13Z","title_canon_sha256":"63b01f0230a10a97c317926df2c273e4f6162b05de2805cdb13291be0633631e"},"schema_version":"1.0","source":{"id":"2310.20091","kind":"arxiv","version":6}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.20091","created_at":"2026-07-05T08:48:58Z"},{"alias_kind":"arxiv_version","alias_value":"2310.20091v6","created_at":"2026-07-05T08:48:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.20091","created_at":"2026-07-05T08:48:58Z"},{"alias_kind":"pith_short_12","alias_value":"KPK4Q4PPAHS6","created_at":"2026-07-05T08:48:58Z"},{"alias_kind":"pith_short_16","alias_value":"KPK4Q4PPAHS6BKCG","created_at":"2026-07-05T08:48:58Z"},{"alias_kind":"pith_short_8","alias_value":"KPK4Q4PP","created_at":"2026-07-05T08:48:58Z"}],"graph_snapshots":[{"event_id":"sha256:2959c5bacd337aafb12be695d434b55a1bfe85ac3ed5d7146bd0fd03c63f10bd","target":"graph","created_at":"2026-07-05T08:48:58Z","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/2310.20091/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Accurate modeling of the diverse and dynamic interests of users remains a significant challenge in the design of personalized recommender systems. Existing user modeling methods, like single-point and multi-point representations, have limitations w.r.t.\\ accuracy, diversity, and adaptability. To overcome these deficiencies, we introduce density-based user representations (DURs), a novel method that leverages Gaussian process regression (GPR) for effective multi-interest recommendation and retrieval. Our approach, GPR4DUR, exploits DURs to capture user interest variability without manual tuning","authors_text":"Craig Boutilier, Fernando Diaz, Haolun Wu, Maryam Karimzadehgan, Masrour Zoghi, Ofer Meshi, Xue Liu","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2023-10-31T00:12:13Z","title":"Density-based User Representation using Gaussian Process Regression for Multi-interest Personalized Retrieval"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.20091","kind":"arxiv","version":6},"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":"sha256:6b88b796db763d87346187794caae52198de9834dbe9391bba36c9e6d62e1282","target":"record","created_at":"2026-07-05T08:48:58Z","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":"02f220d4d40ec2d16afe8f63e3b87a4e342b2a4dacffed20b386ea575840f8d3","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2023-10-31T00:12:13Z","title_canon_sha256":"63b01f0230a10a97c317926df2c273e4f6162b05de2805cdb13291be0633631e"},"schema_version":"1.0","source":{"id":"2310.20091","kind":"arxiv","version":6}},"canonical_sha256":"53d5c871ef01e5e0a846095d119b9e31e12ad7eafd7dcedd31661f4c5e976a12","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"53d5c871ef01e5e0a846095d119b9e31e12ad7eafd7dcedd31661f4c5e976a12","first_computed_at":"2026-07-05T08:48:58.610357Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:48:58.610357Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"yFrJPC4J4KKv65Qwv84avLwrQ4fOj2PkxcSklaiyyK8jnoTuSdHu1SK0YsMuJMg2xh+ntYdRQ6IHVbwx3a3RDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T08:48:58.610809Z","signed_message":"canonical_sha256_bytes"},"source_id":"2310.20091","source_kind":"arxiv","source_version":6}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6b88b796db763d87346187794caae52198de9834dbe9391bba36c9e6d62e1282","sha256:2959c5bacd337aafb12be695d434b55a1bfe85ac3ed5d7146bd0fd03c63f10bd"],"state_sha256":"0cdceaecb803441587ce8c5b78e56780490bd5e7c2565c9ce28e39f18a8e28b9"}