{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:WLFADCSG77WF2OX7U7MUMA43C3","short_pith_number":"pith:WLFADCSG","schema_version":"1.0","canonical_sha256":"b2ca018a46ffec5d3affa7d946039b16cd53a5c7cacb15958018c1fe3b4c0a4a","source":{"kind":"arxiv","id":"1810.00272","version":1},"attestation_state":"computed","paper":{"title":"Detecting Changes in User Preferences using Hidden Markov Models for Sequential Recommendation Tasks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Bamshad Mobasher, Farzad Eskandanian","submitted_at":"2018-09-29T22:33:25Z","abstract_excerpt":"Recommender systems help users find relevant items of interest based on the past preferences of those users. In many domains, however, the tastes and preferences of users change over time due to a variety of factors and recommender systems should capture these dynamics in user preferences in order to remain tuned to the most current interests of users. In this work we present a recommendation framework based on Hidden Markov Models (HMM) which takes into account the dynamics of user preferences. We propose a HMM-based approach to change point detection in the sequence of user interactions whic"},"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":"1810.00272","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2018-09-29T22:33:25Z","cross_cats_sorted":[],"title_canon_sha256":"2be50c248a5267f0094f92a6a6276c2d815cdb2ff1a26729dd2c20d1939b8520","abstract_canon_sha256":"c41c2170e6436883f889ed9603ff94f98561a95001f101f627580681425f88bb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:04:28.320393Z","signature_b64":"cRpdD9qXUM13YJaCGGkWm14hMwn6e9/bC/AHXR5Ux/LzVhwTWwjQzxv9wxESkJTs9/1XCp6Ra7Nu9Y7wbLrxBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b2ca018a46ffec5d3affa7d946039b16cd53a5c7cacb15958018c1fe3b4c0a4a","last_reissued_at":"2026-05-18T00:04:28.319642Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:04:28.319642Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Detecting Changes in User Preferences using Hidden Markov Models for Sequential Recommendation Tasks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Bamshad Mobasher, Farzad Eskandanian","submitted_at":"2018-09-29T22:33:25Z","abstract_excerpt":"Recommender systems help users find relevant items of interest based on the past preferences of those users. In many domains, however, the tastes and preferences of users change over time due to a variety of factors and recommender systems should capture these dynamics in user preferences in order to remain tuned to the most current interests of users. In this work we present a recommendation framework based on Hidden Markov Models (HMM) which takes into account the dynamics of user preferences. We propose a HMM-based approach to change point detection in the sequence of user interactions whic"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.00272","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":""},"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":"1810.00272","created_at":"2026-05-18T00:04:28.319756+00:00"},{"alias_kind":"arxiv_version","alias_value":"1810.00272v1","created_at":"2026-05-18T00:04:28.319756+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.00272","created_at":"2026-05-18T00:04:28.319756+00:00"},{"alias_kind":"pith_short_12","alias_value":"WLFADCSG77WF","created_at":"2026-05-18T12:33:01.666342+00:00"},{"alias_kind":"pith_short_16","alias_value":"WLFADCSG77WF2OX7","created_at":"2026-05-18T12:33:01.666342+00:00"},{"alias_kind":"pith_short_8","alias_value":"WLFADCSG","created_at":"2026-05-18T12:33:01.666342+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/WLFADCSG77WF2OX7U7MUMA43C3","json":"https://pith.science/pith/WLFADCSG77WF2OX7U7MUMA43C3.json","graph_json":"https://pith.science/api/pith-number/WLFADCSG77WF2OX7U7MUMA43C3/graph.json","events_json":"https://pith.science/api/pith-number/WLFADCSG77WF2OX7U7MUMA43C3/events.json","paper":"https://pith.science/paper/WLFADCSG"},"agent_actions":{"view_html":"https://pith.science/pith/WLFADCSG77WF2OX7U7MUMA43C3","download_json":"https://pith.science/pith/WLFADCSG77WF2OX7U7MUMA43C3.json","view_paper":"https://pith.science/paper/WLFADCSG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1810.00272&json=true","fetch_graph":"https://pith.science/api/pith-number/WLFADCSG77WF2OX7U7MUMA43C3/graph.json","fetch_events":"https://pith.science/api/pith-number/WLFADCSG77WF2OX7U7MUMA43C3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WLFADCSG77WF2OX7U7MUMA43C3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WLFADCSG77WF2OX7U7MUMA43C3/action/storage_attestation","attest_author":"https://pith.science/pith/WLFADCSG77WF2OX7U7MUMA43C3/action/author_attestation","sign_citation":"https://pith.science/pith/WLFADCSG77WF2OX7U7MUMA43C3/action/citation_signature","submit_replication":"https://pith.science/pith/WLFADCSG77WF2OX7U7MUMA43C3/action/replication_record"}},"created_at":"2026-05-18T00:04:28.319756+00:00","updated_at":"2026-05-18T00:04:28.319756+00:00"}