{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:YZM7A7L3GDGNJ6OH34CQWZ2GUK","short_pith_number":"pith:YZM7A7L3","canonical_record":{"source":{"id":"1608.07400","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2016-08-26T09:20:21Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"4df37f218658a3d0c33a4027c26919471578209d01b441877ce206fe0eaec77e","abstract_canon_sha256":"8c3472b7164f6f1ea5a4766c4c7b16c3089a665f1c30533f38150e9e06ea3af5"},"schema_version":"1.0"},"canonical_sha256":"c659f07d7b30ccd4f9c7df050b6746a2a993c184b1fe189d3f5e72ca66d537ca","source":{"kind":"arxiv","id":"1608.07400","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1608.07400","created_at":"2026-05-18T00:53:30Z"},{"alias_kind":"arxiv_version","alias_value":"1608.07400v2","created_at":"2026-05-18T00:53:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1608.07400","created_at":"2026-05-18T00:53:30Z"},{"alias_kind":"pith_short_12","alias_value":"YZM7A7L3GDGN","created_at":"2026-05-18T12:30:53Z"},{"alias_kind":"pith_short_16","alias_value":"YZM7A7L3GDGNJ6OH","created_at":"2026-05-18T12:30:53Z"},{"alias_kind":"pith_short_8","alias_value":"YZM7A7L3","created_at":"2026-05-18T12:30:53Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:YZM7A7L3GDGNJ6OH34CQWZ2GUK","target":"record","payload":{"canonical_record":{"source":{"id":"1608.07400","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2016-08-26T09:20:21Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"4df37f218658a3d0c33a4027c26919471578209d01b441877ce206fe0eaec77e","abstract_canon_sha256":"8c3472b7164f6f1ea5a4766c4c7b16c3089a665f1c30533f38150e9e06ea3af5"},"schema_version":"1.0"},"canonical_sha256":"c659f07d7b30ccd4f9c7df050b6746a2a993c184b1fe189d3f5e72ca66d537ca","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:53:30.462189Z","signature_b64":"j/cHRtoIwVT7JhbBF7hhFmKsHpRgiEvNKypMO0eEKojfl29M1K4XkYQdAtIBmx8fK4tYhrLJRjZrRN2XSZMrCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c659f07d7b30ccd4f9c7df050b6746a2a993c184b1fe189d3f5e72ca66d537ca","last_reissued_at":"2026-05-18T00:53:30.461683Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:53:30.461683Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1608.07400","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:53:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6GQ+L75XkQaXvN5SrfHu2vSXv9IsxIJzsCmfVVSTiPpkJxGyZnKZArxtuR2ZMucL9UJX44XtWhMGpTOPxO6ICw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T23:05:27.161102Z"},"content_sha256":"5dc420929cef7930371569fc2708b90c5b32e5865a1745d7c70182a34baa90af","schema_version":"1.0","event_id":"sha256:5dc420929cef7930371569fc2708b90c5b32e5865a1745d7c70182a34baa90af"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:YZM7A7L3GDGNJ6OH34CQWZ2GUK","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Collaborative Filtering with Recurrent Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.IR","authors_text":"Hugues Bersini, Robin Devooght","submitted_at":"2016-08-26T09:20:21Z","abstract_excerpt":"We show that collaborative filtering can be viewed as a sequence prediction problem, and that given this interpretation, recurrent neural networks offer very competitive approach. In particular we study how the long short-term memory (LSTM) can be applied to collaborative filtering, and how it compares to standard nearest neighbors and matrix factorization methods on movie recommendation. We show that the LSTM is competitive in all aspects, and largely outperforms other methods in terms of item coverage and short term predictions."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1608.07400","kind":"arxiv","version":2},"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":"2026-05-18T00:53:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/+32GS619iluvUCljo/wTa2cNSbbzF3Q/6XoE+HaM9haC13tARd3J14SFWiYpmhmMRE/hD58ZZQsTR6OdqblCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T23:05:27.161775Z"},"content_sha256":"8a292d178754470d4004d5e6138d67e7a0fa586cbd5fb25de4c4bd9a4dcbf5bc","schema_version":"1.0","event_id":"sha256:8a292d178754470d4004d5e6138d67e7a0fa586cbd5fb25de4c4bd9a4dcbf5bc"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YZM7A7L3GDGNJ6OH34CQWZ2GUK/bundle.json","state_url":"https://pith.science/pith/YZM7A7L3GDGNJ6OH34CQWZ2GUK/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YZM7A7L3GDGNJ6OH34CQWZ2GUK/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":"2026-06-11T23:05:27Z","links":{"resolver":"https://pith.science/pith/YZM7A7L3GDGNJ6OH34CQWZ2GUK","bundle":"https://pith.science/pith/YZM7A7L3GDGNJ6OH34CQWZ2GUK/bundle.json","state":"https://pith.science/pith/YZM7A7L3GDGNJ6OH34CQWZ2GUK/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YZM7A7L3GDGNJ6OH34CQWZ2GUK/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:YZM7A7L3GDGNJ6OH34CQWZ2GUK","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":"8c3472b7164f6f1ea5a4766c4c7b16c3089a665f1c30533f38150e9e06ea3af5","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2016-08-26T09:20:21Z","title_canon_sha256":"4df37f218658a3d0c33a4027c26919471578209d01b441877ce206fe0eaec77e"},"schema_version":"1.0","source":{"id":"1608.07400","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1608.07400","created_at":"2026-05-18T00:53:30Z"},{"alias_kind":"arxiv_version","alias_value":"1608.07400v2","created_at":"2026-05-18T00:53:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1608.07400","created_at":"2026-05-18T00:53:30Z"},{"alias_kind":"pith_short_12","alias_value":"YZM7A7L3GDGN","created_at":"2026-05-18T12:30:53Z"},{"alias_kind":"pith_short_16","alias_value":"YZM7A7L3GDGNJ6OH","created_at":"2026-05-18T12:30:53Z"},{"alias_kind":"pith_short_8","alias_value":"YZM7A7L3","created_at":"2026-05-18T12:30:53Z"}],"graph_snapshots":[{"event_id":"sha256:8a292d178754470d4004d5e6138d67e7a0fa586cbd5fb25de4c4bd9a4dcbf5bc","target":"graph","created_at":"2026-05-18T00:53:30Z","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":"We show that collaborative filtering can be viewed as a sequence prediction problem, and that given this interpretation, recurrent neural networks offer very competitive approach. In particular we study how the long short-term memory (LSTM) can be applied to collaborative filtering, and how it compares to standard nearest neighbors and matrix factorization methods on movie recommendation. We show that the LSTM is competitive in all aspects, and largely outperforms other methods in terms of item coverage and short term predictions.","authors_text":"Hugues Bersini, Robin Devooght","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2016-08-26T09:20:21Z","title":"Collaborative Filtering with Recurrent Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1608.07400","kind":"arxiv","version":2},"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":"sha256:5dc420929cef7930371569fc2708b90c5b32e5865a1745d7c70182a34baa90af","target":"record","created_at":"2026-05-18T00:53:30Z","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":"8c3472b7164f6f1ea5a4766c4c7b16c3089a665f1c30533f38150e9e06ea3af5","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2016-08-26T09:20:21Z","title_canon_sha256":"4df37f218658a3d0c33a4027c26919471578209d01b441877ce206fe0eaec77e"},"schema_version":"1.0","source":{"id":"1608.07400","kind":"arxiv","version":2}},"canonical_sha256":"c659f07d7b30ccd4f9c7df050b6746a2a993c184b1fe189d3f5e72ca66d537ca","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c659f07d7b30ccd4f9c7df050b6746a2a993c184b1fe189d3f5e72ca66d537ca","first_computed_at":"2026-05-18T00:53:30.461683Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:53:30.461683Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"j/cHRtoIwVT7JhbBF7hhFmKsHpRgiEvNKypMO0eEKojfl29M1K4XkYQdAtIBmx8fK4tYhrLJRjZrRN2XSZMrCg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:53:30.462189Z","signed_message":"canonical_sha256_bytes"},"source_id":"1608.07400","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5dc420929cef7930371569fc2708b90c5b32e5865a1745d7c70182a34baa90af","sha256:8a292d178754470d4004d5e6138d67e7a0fa586cbd5fb25de4c4bd9a4dcbf5bc"],"state_sha256":"9c44ecdc1ecab2a2381bef15bb40770d948bb86a234a540aa1b18d8c8cfd6828"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dl0cp/9AfoXfPxQgT/o0F0qyWpHdJ3bwfZXKI6RIV6n4iOILslTmCJcQ231EHYXVMBXJuVCCBlNK/8fzK9jfDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-11T23:05:27.165788Z","bundle_sha256":"21866132c0c5f1740ef7bfe7688083e8fb222f04c1e5967c042d16e0c839d83d"}}