{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:3KYXW4WUV3MDTVKW2HB6LIYVWL","short_pith_number":"pith:3KYXW4WU","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"},"canonical_sha256":"dab17b72d4aed839d556d1c3e5a315b2da440645532d60579c36fef379a0fb63","source":{"kind":"arxiv","id":"2301.03881","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2301.03881","created_at":"2026-07-05T05:32:07Z"},{"alias_kind":"arxiv_version","alias_value":"2301.03881v1","created_at":"2026-07-05T05:32:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2301.03881","created_at":"2026-07-05T05:32:07Z"},{"alias_kind":"pith_short_12","alias_value":"3KYXW4WUV3MD","created_at":"2026-07-05T05:32:07Z"},{"alias_kind":"pith_short_16","alias_value":"3KYXW4WUV3MDTVKW","created_at":"2026-07-05T05:32:07Z"},{"alias_kind":"pith_short_8","alias_value":"3KYXW4WU","created_at":"2026-07-05T05:32:07Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:3KYXW4WUV3MDTVKW2HB6LIYVWL","target":"record","payload":{"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"},"canonical_sha256":"dab17b72d4aed839d556d1c3e5a315b2da440645532d60579c36fef379a0fb63","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"},"source_kind":"arxiv","source_id":"2301.03881","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":"2026-07-05T05:32:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pKjfmRRazookPJ8kv9wQ0rnLuov1TCl8RQvupNhPyTTJGoK9VfEtr8Lg1nHQ6aKKn9zaZW3t1SGAgXDEjIxYCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-23T10:08:49.940595Z"},"content_sha256":"51a4d6d276f3eb6ef276c7cb89189587adc66705f80afef418c4bf3a4141d25c","schema_version":"1.0","event_id":"sha256:51a4d6d276f3eb6ef276c7cb89189587adc66705f80afef418c4bf3a4141d25c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:3KYXW4WUV3MDTVKW2HB6LIYVWL","target":"graph","payload":{"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T05:32:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5Ac13JCNe7WGU5qeCyGJ6i39cnyAOKGo1GPDqhNg62AQcnGitajWI99k3iLbMvUuKuVR54JR+w/RW2qv8iSZDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-23T10:08:49.940932Z"},"content_sha256":"77cb25bc53d1e72a6047ad920b495aa4a1d5e9680d19c24cea1675a6568056e3","schema_version":"1.0","event_id":"sha256:77cb25bc53d1e72a6047ad920b495aa4a1d5e9680d19c24cea1675a6568056e3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/3KYXW4WUV3MDTVKW2HB6LIYVWL/bundle.json","state_url":"https://pith.science/pith/3KYXW4WUV3MDTVKW2HB6LIYVWL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/3KYXW4WUV3MDTVKW2HB6LIYVWL/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":"2026-08-23T10:08:49Z","links":{"resolver":"https://pith.science/pith/3KYXW4WUV3MDTVKW2HB6LIYVWL","bundle":"https://pith.science/pith/3KYXW4WUV3MDTVKW2HB6LIYVWL/bundle.json","state":"https://pith.science/pith/3KYXW4WUV3MDTVKW2HB6LIYVWL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/3KYXW4WUV3MDTVKW2HB6LIYVWL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:3KYXW4WUV3MDTVKW2HB6LIYVWL","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":"fde0159e260ce8b2f9ba2d96f2c704e1feba96415b67a3acfb59c410c699c3f9","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2023-01-10T10:07:29Z","title_canon_sha256":"5ed939ee9ff7dfa7db8a268fcf788e9ac204fa8332ae317c7f8d5a1b867788e0"},"schema_version":"1.0","source":{"id":"2301.03881","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2301.03881","created_at":"2026-07-05T05:32:07Z"},{"alias_kind":"arxiv_version","alias_value":"2301.03881v1","created_at":"2026-07-05T05:32:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2301.03881","created_at":"2026-07-05T05:32:07Z"},{"alias_kind":"pith_short_12","alias_value":"3KYXW4WUV3MD","created_at":"2026-07-05T05:32:07Z"},{"alias_kind":"pith_short_16","alias_value":"3KYXW4WUV3MDTVKW","created_at":"2026-07-05T05:32:07Z"},{"alias_kind":"pith_short_8","alias_value":"3KYXW4WU","created_at":"2026-07-05T05:32:07Z"}],"graph_snapshots":[{"event_id":"sha256:77cb25bc53d1e72a6047ad920b495aa4a1d5e9680d19c24cea1675a6568056e3","target":"graph","created_at":"2026-07-05T05:32:07Z","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/2301.03881/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"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","authors_text":"Crawford Revie, Francesco Meggetto, John Levine, Yashar Moshfeghi","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2023-01-10T10:07:29Z","title":"Why People Skip Music? On Predicting Music Skips using Deep Reinforcement Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2301.03881","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":"sha256:51a4d6d276f3eb6ef276c7cb89189587adc66705f80afef418c4bf3a4141d25c","target":"record","created_at":"2026-07-05T05:32:07Z","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":"fde0159e260ce8b2f9ba2d96f2c704e1feba96415b67a3acfb59c410c699c3f9","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2023-01-10T10:07:29Z","title_canon_sha256":"5ed939ee9ff7dfa7db8a268fcf788e9ac204fa8332ae317c7f8d5a1b867788e0"},"schema_version":"1.0","source":{"id":"2301.03881","kind":"arxiv","version":1}},"canonical_sha256":"dab17b72d4aed839d556d1c3e5a315b2da440645532d60579c36fef379a0fb63","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"dab17b72d4aed839d556d1c3e5a315b2da440645532d60579c36fef379a0fb63","first_computed_at":"2026-07-05T05:32:07.171305Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:32:07.171305Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"wKSUx228aHMXzTFUk4bezX2lvtdTsD3LVVNA3o+B0ND+7tj20xCFvta9k+sjGm2Y51pLKSLhbwkCzj+SS49YAw==","signature_status":"signed_v1","signed_at":"2026-07-05T05:32:07.171783Z","signed_message":"canonical_sha256_bytes"},"source_id":"2301.03881","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:51a4d6d276f3eb6ef276c7cb89189587adc66705f80afef418c4bf3a4141d25c","sha256:77cb25bc53d1e72a6047ad920b495aa4a1d5e9680d19c24cea1675a6568056e3"],"state_sha256":"9daebb1dd1cbd0ab67c9a1e7b53ebd71fb18d5e7b835b9839764eadb57875603"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YG8dvZjGra/nJqRfBV3WcAK0GsXW4fBnGcwF/bcNnDP8aB2MNrMr0SsR7KGejt42wLRpu0263+CszeJw1o2hDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-23T10:08:49.944576Z","bundle_sha256":"fd6476eb69e719332d5007f4110d823fd49061e45da2e1fc2e5564cfca462066"}}