{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2013:KMYZ37BOX7K7TQ3X6U75D67N56","short_pith_number":"pith:KMYZ37BO","canonical_record":{"source":{"id":"1311.6355","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.MM","submitted_at":"2013-11-06T12:20:35Z","cross_cats_sorted":["cs.IR","cs.LG"],"title_canon_sha256":"04182c19717edf666023be6efee8461ea80ba9f6ce45829263673e0bbaf181f6","abstract_canon_sha256":"ebcf82342c349316198feff9e2d141eb21b49569d9a4ea5020d99ba0370e5fe3"},"schema_version":"1.0"},"canonical_sha256":"53319dfc2ebfd5f9c377f53fd1fbedefb53bcb8bcda669b78a0bcebaf3df2656","source":{"kind":"arxiv","id":"1311.6355","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1311.6355","created_at":"2026-05-18T03:06:14Z"},{"alias_kind":"arxiv_version","alias_value":"1311.6355v1","created_at":"2026-05-18T03:06:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1311.6355","created_at":"2026-05-18T03:06:14Z"},{"alias_kind":"pith_short_12","alias_value":"KMYZ37BOX7K7","created_at":"2026-05-18T12:27:51Z"},{"alias_kind":"pith_short_16","alias_value":"KMYZ37BOX7K7TQ3X","created_at":"2026-05-18T12:27:51Z"},{"alias_kind":"pith_short_8","alias_value":"KMYZ37BO","created_at":"2026-05-18T12:27:51Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2013:KMYZ37BOX7K7TQ3X6U75D67N56","target":"record","payload":{"canonical_record":{"source":{"id":"1311.6355","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.MM","submitted_at":"2013-11-06T12:20:35Z","cross_cats_sorted":["cs.IR","cs.LG"],"title_canon_sha256":"04182c19717edf666023be6efee8461ea80ba9f6ce45829263673e0bbaf181f6","abstract_canon_sha256":"ebcf82342c349316198feff9e2d141eb21b49569d9a4ea5020d99ba0370e5fe3"},"schema_version":"1.0"},"canonical_sha256":"53319dfc2ebfd5f9c377f53fd1fbedefb53bcb8bcda669b78a0bcebaf3df2656","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:06:14.288504Z","signature_b64":"6A22cNxVXJIBRjhqRei5AYabDD5tIQ0ZeGfVHiPnITyWYSFdkC+KjuPJPi4R01uQMc9f5tGizQ1x9AHt325DCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"53319dfc2ebfd5f9c377f53fd1fbedefb53bcb8bcda669b78a0bcebaf3df2656","last_reissued_at":"2026-05-18T03:06:14.288021Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:06:14.288021Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1311.6355","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":"2026-05-18T03:06:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9D+k/S4UuouJs+9YM02NvZ8xAi/UUN/aNQsBRQtcTUeXrZ1OB86AyOjtrNdgUN/kYQUF0LinMDS706t6UMU/DA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T22:08:24.007044Z"},"content_sha256":"01c78d5b77ed7184bf85ab511863711bd1289172d677495833cab02878185deb","schema_version":"1.0","event_id":"sha256:01c78d5b77ed7184bf85ab511863711bd1289172d677495833cab02878185deb"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2013:KMYZ37BOX7K7TQ3X6U75D67N56","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Exploration in Interactive Personalized Music Recommendation: A Reinforcement Learning Approach","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IR","cs.LG"],"primary_cat":"cs.MM","authors_text":"David Hsu, Xinxi Wang, Ye Wang, Yi Wang","submitted_at":"2013-11-06T12:20:35Z","abstract_excerpt":"Current music recommender systems typically act in a greedy fashion by recommending songs with the highest user ratings. Greedy recommendation, however, is suboptimal over the long term: it does not actively gather information on user preferences and fails to recommend novel songs that are potentially interesting. A successful recommender system must balance the needs to explore user preferences and to exploit this information for recommendation. This paper presents a new approach to music recommendation by formulating this exploration-exploitation trade-off as a reinforcement learning task ca"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1311.6355","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T03:06:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JPBkhxMIX+hEcRH5TgFYF324d4P074wwkGwMVK0SUR3nNokCr+WoTQ2DaoXo2Y5FA18JCQ19C7GTHJxSOalyAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T22:08:24.007728Z"},"content_sha256":"355a514266d2240eaa9543a6f07ba05a127ce4bf27106b89d033f8b97c3dc501","schema_version":"1.0","event_id":"sha256:355a514266d2240eaa9543a6f07ba05a127ce4bf27106b89d033f8b97c3dc501"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/KMYZ37BOX7K7TQ3X6U75D67N56/bundle.json","state_url":"https://pith.science/pith/KMYZ37BOX7K7TQ3X6U75D67N56/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/KMYZ37BOX7K7TQ3X6U75D67N56/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":"2026-06-11T22:08:24Z","links":{"resolver":"https://pith.science/pith/KMYZ37BOX7K7TQ3X6U75D67N56","bundle":"https://pith.science/pith/KMYZ37BOX7K7TQ3X6U75D67N56/bundle.json","state":"https://pith.science/pith/KMYZ37BOX7K7TQ3X6U75D67N56/state.json","well_known_bundle":"https://pith.science/.well-known/pith/KMYZ37BOX7K7TQ3X6U75D67N56/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2013:KMYZ37BOX7K7TQ3X6U75D67N56","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":"ebcf82342c349316198feff9e2d141eb21b49569d9a4ea5020d99ba0370e5fe3","cross_cats_sorted":["cs.IR","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.MM","submitted_at":"2013-11-06T12:20:35Z","title_canon_sha256":"04182c19717edf666023be6efee8461ea80ba9f6ce45829263673e0bbaf181f6"},"schema_version":"1.0","source":{"id":"1311.6355","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1311.6355","created_at":"2026-05-18T03:06:14Z"},{"alias_kind":"arxiv_version","alias_value":"1311.6355v1","created_at":"2026-05-18T03:06:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1311.6355","created_at":"2026-05-18T03:06:14Z"},{"alias_kind":"pith_short_12","alias_value":"KMYZ37BOX7K7","created_at":"2026-05-18T12:27:51Z"},{"alias_kind":"pith_short_16","alias_value":"KMYZ37BOX7K7TQ3X","created_at":"2026-05-18T12:27:51Z"},{"alias_kind":"pith_short_8","alias_value":"KMYZ37BO","created_at":"2026-05-18T12:27:51Z"}],"graph_snapshots":[{"event_id":"sha256:355a514266d2240eaa9543a6f07ba05a127ce4bf27106b89d033f8b97c3dc501","target":"graph","created_at":"2026-05-18T03:06:14Z","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"},"paper":{"abstract_excerpt":"Current music recommender systems typically act in a greedy fashion by recommending songs with the highest user ratings. Greedy recommendation, however, is suboptimal over the long term: it does not actively gather information on user preferences and fails to recommend novel songs that are potentially interesting. A successful recommender system must balance the needs to explore user preferences and to exploit this information for recommendation. This paper presents a new approach to music recommendation by formulating this exploration-exploitation trade-off as a reinforcement learning task ca","authors_text":"David Hsu, Xinxi Wang, Ye Wang, Yi Wang","cross_cats":["cs.IR","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.MM","submitted_at":"2013-11-06T12:20:35Z","title":"Exploration in Interactive Personalized Music Recommendation: A Reinforcement Learning Approach"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1311.6355","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":"sha256:01c78d5b77ed7184bf85ab511863711bd1289172d677495833cab02878185deb","target":"record","created_at":"2026-05-18T03:06:14Z","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":"ebcf82342c349316198feff9e2d141eb21b49569d9a4ea5020d99ba0370e5fe3","cross_cats_sorted":["cs.IR","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.MM","submitted_at":"2013-11-06T12:20:35Z","title_canon_sha256":"04182c19717edf666023be6efee8461ea80ba9f6ce45829263673e0bbaf181f6"},"schema_version":"1.0","source":{"id":"1311.6355","kind":"arxiv","version":1}},"canonical_sha256":"53319dfc2ebfd5f9c377f53fd1fbedefb53bcb8bcda669b78a0bcebaf3df2656","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"53319dfc2ebfd5f9c377f53fd1fbedefb53bcb8bcda669b78a0bcebaf3df2656","first_computed_at":"2026-05-18T03:06:14.288021Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T03:06:14.288021Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"6A22cNxVXJIBRjhqRei5AYabDD5tIQ0ZeGfVHiPnITyWYSFdkC+KjuPJPi4R01uQMc9f5tGizQ1x9AHt325DCA==","signature_status":"signed_v1","signed_at":"2026-05-18T03:06:14.288504Z","signed_message":"canonical_sha256_bytes"},"source_id":"1311.6355","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:01c78d5b77ed7184bf85ab511863711bd1289172d677495833cab02878185deb","sha256:355a514266d2240eaa9543a6f07ba05a127ce4bf27106b89d033f8b97c3dc501"],"state_sha256":"b1b85f4e6bc9937a05b77c092fe42b045c4438235627b5430fd2e62d116ceb6e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"K/qsDg8lo6wCOZMw1wd1ik3gqNglDN2Gf93Pyom6Y4TakjSTFsW9EoG1s7c+Gi5u1k1vHHivV5t8Eq8GcRWIAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-11T22:08:24.011667Z","bundle_sha256":"1c85b584c83190f48f51cc8b95791bb6158497c050317b3466298da31a1f3973"}}