{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:3KYXW4WUV3MDTVKW2HB6LIYVWL","short_pith_number":"pith:3KYXW4WU","schema_version":"1.0","canonical_sha256":"dab17b72d4aed839d556d1c3e5a315b2da440645532d60579c36fef379a0fb63","source":{"kind":"arxiv","id":"2301.03881","version":1},"attestation_state":"computed","paper":{"title":"Why People Skip Music? On Predicting Music Skips using Deep Reinforcement Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Crawford Revie, Francesco Meggetto, John Levine, Yashar Moshfeghi","submitted_at":"2023-01-10T10:07:29Z","abstract_excerpt":"Music recommender systems are an integral part of our daily life. Recent research has seen a significant effort around black-box recommender based approaches such as Deep Reinforcement Learning (DRL). These advances have led, together with the increasing concerns around users' data collection and privacy, to a strong interest in building responsible recommender systems. A key element of a successful music recommender system is modelling how users interact with streamed content. By first understanding these interactions, insights can be drawn to enable the construction of more transparent and r"},"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":"2301.03881","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2023-01-10T10:07:29Z","cross_cats_sorted":[],"title_canon_sha256":"5ed939ee9ff7dfa7db8a268fcf788e9ac204fa8332ae317c7f8d5a1b867788e0","abstract_canon_sha256":"fde0159e260ce8b2f9ba2d96f2c704e1feba96415b67a3acfb59c410c699c3f9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:32:07.171783Z","signature_b64":"wKSUx228aHMXzTFUk4bezX2lvtdTsD3LVVNA3o+B0ND+7tj20xCFvta9k+sjGm2Y51pLKSLhbwkCzj+SS49YAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dab17b72d4aed839d556d1c3e5a315b2da440645532d60579c36fef379a0fb63","last_reissued_at":"2026-07-05T05:32:07.171305Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:32:07.171305Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Why People Skip Music? On Predicting Music Skips using Deep Reinforcement Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Crawford Revie, Francesco Meggetto, John Levine, Yashar Moshfeghi","submitted_at":"2023-01-10T10:07:29Z","abstract_excerpt":"Music recommender systems are an integral part of our daily life. Recent research has seen a significant effort around black-box recommender based approaches such as Deep Reinforcement Learning (DRL). These advances have led, together with the increasing concerns around users' data collection and privacy, to a strong interest in building responsible recommender systems. A key element of a successful music recommender system is modelling how users interact with streamed content. By first understanding these interactions, insights can be drawn to enable the construction of more transparent and r"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2301.03881","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/2301.03881/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":"2301.03881","created_at":"2026-07-05T05:32:07.171376+00:00"},{"alias_kind":"arxiv_version","alias_value":"2301.03881v1","created_at":"2026-07-05T05:32:07.171376+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2301.03881","created_at":"2026-07-05T05:32:07.171376+00:00"},{"alias_kind":"pith_short_12","alias_value":"3KYXW4WUV3MD","created_at":"2026-07-05T05:32:07.171376+00:00"},{"alias_kind":"pith_short_16","alias_value":"3KYXW4WUV3MDTVKW","created_at":"2026-07-05T05:32:07.171376+00:00"},{"alias_kind":"pith_short_8","alias_value":"3KYXW4WU","created_at":"2026-07-05T05:32:07.171376+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2608.06106","citing_title":"The Algorithmic Flattening of Sound: Computational Evidence and Justice Implications of AI Music Homogenization","ref_index":118,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/3KYXW4WUV3MDTVKW2HB6LIYVWL","json":"https://pith.science/pith/3KYXW4WUV3MDTVKW2HB6LIYVWL.json","graph_json":"https://pith.science/api/pith-number/3KYXW4WUV3MDTVKW2HB6LIYVWL/graph.json","events_json":"https://pith.science/api/pith-number/3KYXW4WUV3MDTVKW2HB6LIYVWL/events.json","paper":"https://pith.science/paper/3KYXW4WU"},"agent_actions":{"view_html":"https://pith.science/pith/3KYXW4WUV3MDTVKW2HB6LIYVWL","download_json":"https://pith.science/pith/3KYXW4WUV3MDTVKW2HB6LIYVWL.json","view_paper":"https://pith.science/paper/3KYXW4WU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2301.03881&json=true","fetch_graph":"https://pith.science/api/pith-number/3KYXW4WUV3MDTVKW2HB6LIYVWL/graph.json","fetch_events":"https://pith.science/api/pith-number/3KYXW4WUV3MDTVKW2HB6LIYVWL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3KYXW4WUV3MDTVKW2HB6LIYVWL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3KYXW4WUV3MDTVKW2HB6LIYVWL/action/storage_attestation","attest_author":"https://pith.science/pith/3KYXW4WUV3MDTVKW2HB6LIYVWL/action/author_attestation","sign_citation":"https://pith.science/pith/3KYXW4WUV3MDTVKW2HB6LIYVWL/action/citation_signature","submit_replication":"https://pith.science/pith/3KYXW4WUV3MDTVKW2HB6LIYVWL/action/replication_record"}},"created_at":"2026-07-05T05:32:07.171376+00:00","updated_at":"2026-07-05T05:32:07.171376+00:00"}