{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:I5LANBBIMQFOBVDMKRSWL5VP4J","short_pith_number":"pith:I5LANBBI","canonical_record":{"source":{"id":"1602.05436","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-02-17T14:40:52Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"5a21ed82fedb306e38b6d28cc0b2141843eaf61ca1fdce7bf1b427419cd8e101","abstract_canon_sha256":"4201da5054771db0afd777ed206fdba97ffa016f3d06f00048fe6bbc3a3cd425"},"schema_version":"1.0"},"canonical_sha256":"4756068428640ae0d46c546565f6afe2726d4208df1496175c8d045634f9585a","source":{"kind":"arxiv","id":"1602.05436","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1602.05436","created_at":"2026-05-18T01:20:28Z"},{"alias_kind":"arxiv_version","alias_value":"1602.05436v1","created_at":"2026-05-18T01:20:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1602.05436","created_at":"2026-05-18T01:20:28Z"},{"alias_kind":"pith_short_12","alias_value":"I5LANBBIMQFO","created_at":"2026-05-18T12:30:22Z"},{"alias_kind":"pith_short_16","alias_value":"I5LANBBIMQFOBVDM","created_at":"2026-05-18T12:30:22Z"},{"alias_kind":"pith_short_8","alias_value":"I5LANBBI","created_at":"2026-05-18T12:30:22Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:I5LANBBIMQFOBVDMKRSWL5VP4J","target":"record","payload":{"canonical_record":{"source":{"id":"1602.05436","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-02-17T14:40:52Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"5a21ed82fedb306e38b6d28cc0b2141843eaf61ca1fdce7bf1b427419cd8e101","abstract_canon_sha256":"4201da5054771db0afd777ed206fdba97ffa016f3d06f00048fe6bbc3a3cd425"},"schema_version":"1.0"},"canonical_sha256":"4756068428640ae0d46c546565f6afe2726d4208df1496175c8d045634f9585a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:20:28.211737Z","signature_b64":"huDKpkMQCbxNClF2euOYahzLm2KYeCQWvvI2M0KlRNMvoCtMVSwqCuQAq7ZzOsHmLU5WigMuiKRRbJ9OmHGsBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4756068428640ae0d46c546565f6afe2726d4208df1496175c8d045634f9585a","last_reissued_at":"2026-05-18T01:20:28.211138Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:20:28.211138Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1602.05436","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":"2026-05-18T01:20:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+9qrbke8JQibwz8y05khLqHEJ3pvVkuyBGiA+hMYPM10KQ9Op24EeuvBAkxCWNEceFXR7DPGNbLh5jd64SGYBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T21:17:24.344615Z"},"content_sha256":"22902d8ed73d675d6bf0e7d8a046ab6f12624f9cadc7a1236a784fb5be18524d","schema_version":"1.0","event_id":"sha256:22902d8ed73d675d6bf0e7d8a046ab6f12624f9cadc7a1236a784fb5be18524d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:I5LANBBIMQFOBVDMKRSWL5VP4J","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Low-Rank Factorization of Determinantal Point Processes for Recommendation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Mike Gartrell, Noam Koenigstein, Ulrich Paquet","submitted_at":"2016-02-17T14:40:52Z","abstract_excerpt":"Determinantal point processes (DPPs) have garnered attention as an elegant probabilistic model of set diversity. They are useful for a number of subset selection tasks, including product recommendation. DPPs are parametrized by a positive semi-definite kernel matrix. In this work we present a new method for learning the DPP kernel from observed data using a low-rank factorization of this kernel. We show that this low-rank factorization enables a learning algorithm that is nearly an order of magnitude faster than previous approaches, while also providing for a method for computing product recom"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1602.05436","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":"2026-05-18T01:20:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5bXYMzHOXTYhiQEgo8QVu2PNURrzUVaYLNNZUY4qmXN6lH4E7FyLEsN3cnPWWWmGBBqWKR1YedYo4pPWAyNBDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T21:17:24.345228Z"},"content_sha256":"1e6e92219f9942f5ae0da06dac7f6e5c1dd84f927ef3373c76a045300b8decff","schema_version":"1.0","event_id":"sha256:1e6e92219f9942f5ae0da06dac7f6e5c1dd84f927ef3373c76a045300b8decff"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/I5LANBBIMQFOBVDMKRSWL5VP4J/bundle.json","state_url":"https://pith.science/pith/I5LANBBIMQFOBVDMKRSWL5VP4J/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/I5LANBBIMQFOBVDMKRSWL5VP4J/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":"2026-05-28T21:17:24Z","links":{"resolver":"https://pith.science/pith/I5LANBBIMQFOBVDMKRSWL5VP4J","bundle":"https://pith.science/pith/I5LANBBIMQFOBVDMKRSWL5VP4J/bundle.json","state":"https://pith.science/pith/I5LANBBIMQFOBVDMKRSWL5VP4J/state.json","well_known_bundle":"https://pith.science/.well-known/pith/I5LANBBIMQFOBVDMKRSWL5VP4J/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:I5LANBBIMQFOBVDMKRSWL5VP4J","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":"4201da5054771db0afd777ed206fdba97ffa016f3d06f00048fe6bbc3a3cd425","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-02-17T14:40:52Z","title_canon_sha256":"5a21ed82fedb306e38b6d28cc0b2141843eaf61ca1fdce7bf1b427419cd8e101"},"schema_version":"1.0","source":{"id":"1602.05436","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1602.05436","created_at":"2026-05-18T01:20:28Z"},{"alias_kind":"arxiv_version","alias_value":"1602.05436v1","created_at":"2026-05-18T01:20:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1602.05436","created_at":"2026-05-18T01:20:28Z"},{"alias_kind":"pith_short_12","alias_value":"I5LANBBIMQFO","created_at":"2026-05-18T12:30:22Z"},{"alias_kind":"pith_short_16","alias_value":"I5LANBBIMQFOBVDM","created_at":"2026-05-18T12:30:22Z"},{"alias_kind":"pith_short_8","alias_value":"I5LANBBI","created_at":"2026-05-18T12:30:22Z"}],"graph_snapshots":[{"event_id":"sha256:1e6e92219f9942f5ae0da06dac7f6e5c1dd84f927ef3373c76a045300b8decff","target":"graph","created_at":"2026-05-18T01:20:28Z","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":"Determinantal point processes (DPPs) have garnered attention as an elegant probabilistic model of set diversity. They are useful for a number of subset selection tasks, including product recommendation. DPPs are parametrized by a positive semi-definite kernel matrix. In this work we present a new method for learning the DPP kernel from observed data using a low-rank factorization of this kernel. We show that this low-rank factorization enables a learning algorithm that is nearly an order of magnitude faster than previous approaches, while also providing for a method for computing product recom","authors_text":"Mike Gartrell, Noam Koenigstein, Ulrich Paquet","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-02-17T14:40:52Z","title":"Low-Rank Factorization of Determinantal Point Processes for Recommendation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1602.05436","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":"sha256:22902d8ed73d675d6bf0e7d8a046ab6f12624f9cadc7a1236a784fb5be18524d","target":"record","created_at":"2026-05-18T01:20:28Z","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":"4201da5054771db0afd777ed206fdba97ffa016f3d06f00048fe6bbc3a3cd425","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-02-17T14:40:52Z","title_canon_sha256":"5a21ed82fedb306e38b6d28cc0b2141843eaf61ca1fdce7bf1b427419cd8e101"},"schema_version":"1.0","source":{"id":"1602.05436","kind":"arxiv","version":1}},"canonical_sha256":"4756068428640ae0d46c546565f6afe2726d4208df1496175c8d045634f9585a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4756068428640ae0d46c546565f6afe2726d4208df1496175c8d045634f9585a","first_computed_at":"2026-05-18T01:20:28.211138Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:20:28.211138Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"huDKpkMQCbxNClF2euOYahzLm2KYeCQWvvI2M0KlRNMvoCtMVSwqCuQAq7ZzOsHmLU5WigMuiKRRbJ9OmHGsBg==","signature_status":"signed_v1","signed_at":"2026-05-18T01:20:28.211737Z","signed_message":"canonical_sha256_bytes"},"source_id":"1602.05436","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:22902d8ed73d675d6bf0e7d8a046ab6f12624f9cadc7a1236a784fb5be18524d","sha256:1e6e92219f9942f5ae0da06dac7f6e5c1dd84f927ef3373c76a045300b8decff"],"state_sha256":"cad31e9a660cca8acd8a8b891b12dc213877b1ec1217d7c57544d26124d515af"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LIpM6SBrdr3GGL2/e3JaV3U7/i0CgRWeOlS974cYQuYLFjn0PpO93s4OnicieI3dyKw9SA4OPllkAot86POuCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-28T21:17:24.348769Z","bundle_sha256":"5f71d60facc7671b6bdea636b7275ec6aedb2526d95310db809f2428c7d15949"}}