{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:F6LCTXLM6ZDP7OIWQOXYWICWWK","short_pith_number":"pith:F6LCTXLM","canonical_record":{"source":{"id":"1606.07674","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2016-06-24T13:10:50Z","cross_cats_sorted":[],"title_canon_sha256":"791e8d6633cf909370a3f91f6e3e699be6b945cbc05f503e6f2b018b2a395310","abstract_canon_sha256":"a67e4163a9ba0aed9fe8b847dd460a1225acaf1aa055c9c800b74ed3ffe8a17e"},"schema_version":"1.0"},"canonical_sha256":"2f9629dd6cf646ffb91683af8b2056b2bbff3e1c13f1a68915899f05bf3c1fc9","source":{"kind":"arxiv","id":"1606.07674","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1606.07674","created_at":"2026-05-18T01:04:43Z"},{"alias_kind":"arxiv_version","alias_value":"1606.07674v2","created_at":"2026-05-18T01:04:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1606.07674","created_at":"2026-05-18T01:04:43Z"},{"alias_kind":"pith_short_12","alias_value":"F6LCTXLM6ZDP","created_at":"2026-05-18T12:30:15Z"},{"alias_kind":"pith_short_16","alias_value":"F6LCTXLM6ZDP7OIW","created_at":"2026-05-18T12:30:15Z"},{"alias_kind":"pith_short_8","alias_value":"F6LCTXLM","created_at":"2026-05-18T12:30:15Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:F6LCTXLM6ZDP7OIWQOXYWICWWK","target":"record","payload":{"canonical_record":{"source":{"id":"1606.07674","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2016-06-24T13:10:50Z","cross_cats_sorted":[],"title_canon_sha256":"791e8d6633cf909370a3f91f6e3e699be6b945cbc05f503e6f2b018b2a395310","abstract_canon_sha256":"a67e4163a9ba0aed9fe8b847dd460a1225acaf1aa055c9c800b74ed3ffe8a17e"},"schema_version":"1.0"},"canonical_sha256":"2f9629dd6cf646ffb91683af8b2056b2bbff3e1c13f1a68915899f05bf3c1fc9","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:04:43.234805Z","signature_b64":"C2F7w58mYguO1+HoM9IkMCE/x2DBuwXSDwsjsfnQLYP04lZ1Ds4LxCs4ttiADaYNG/PFC+G3zPspSNynQgyiCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2f9629dd6cf646ffb91683af8b2056b2bbff3e1c13f1a68915899f05bf3c1fc9","last_reissued_at":"2026-05-18T01:04:43.234171Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:04:43.234171Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1606.07674","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":"2026-05-18T01:04:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pYa63ZRK8Fp8J0Pp3NSwSzWT6BOEqsO4/gIBmeFp/eG1/+r5Vq1uWlUoecTH+1fmP18pnTVxXbia0EcupE0eAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T19:25:58.965398Z"},"content_sha256":"0046ec22a84e014e4582b5253348eb011620612dfac766c250cce79a4b225278","schema_version":"1.0","event_id":"sha256:0046ec22a84e014e4582b5253348eb011620612dfac766c250cce79a4b225278"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:F6LCTXLM6ZDP7OIWQOXYWICWWK","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Neural Autoregressive Collaborative Filtering for Implicit Feedback","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Bangsheng Tang, Cailiang Liu, Hanning Zhou, Yin Zheng","submitted_at":"2016-06-24T13:10:50Z","abstract_excerpt":"This paper proposes implicit CF-NADE, a neural autoregressive model for collaborative filtering tasks using implicit feedback ( e.g. click, watch, browse behaviors). We first convert a users implicit feedback into a like vector and a confidence vector, and then model the probability of the like vector, weighted by the confidence vector. The training objective of implicit CF-NADE is to maximize a weighted negative log-likelihood. We test the performance of implicit CF-NADE on a dataset collected from a popular digital TV streaming service. More specifically, in the experiments, we describe how "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1606.07674","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":"2026-05-18T01:04:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BfffE+/Y1wBlZTpSI5mrQV7/7Ue+pxItqY4VBTMFfu5zP2DsWryVPTEYKnBN7D5HrpkUCfimE1UwyqfdWdL1CA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T19:25:58.966174Z"},"content_sha256":"8dc59b7c6748b59e7b0978601e8e6fc26cef7c589c881e0d188dec276dd316fc","schema_version":"1.0","event_id":"sha256:8dc59b7c6748b59e7b0978601e8e6fc26cef7c589c881e0d188dec276dd316fc"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/F6LCTXLM6ZDP7OIWQOXYWICWWK/bundle.json","state_url":"https://pith.science/pith/F6LCTXLM6ZDP7OIWQOXYWICWWK/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/F6LCTXLM6ZDP7OIWQOXYWICWWK/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":"2026-05-31T19:25:58Z","links":{"resolver":"https://pith.science/pith/F6LCTXLM6ZDP7OIWQOXYWICWWK","bundle":"https://pith.science/pith/F6LCTXLM6ZDP7OIWQOXYWICWWK/bundle.json","state":"https://pith.science/pith/F6LCTXLM6ZDP7OIWQOXYWICWWK/state.json","well_known_bundle":"https://pith.science/.well-known/pith/F6LCTXLM6ZDP7OIWQOXYWICWWK/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:F6LCTXLM6ZDP7OIWQOXYWICWWK","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":"a67e4163a9ba0aed9fe8b847dd460a1225acaf1aa055c9c800b74ed3ffe8a17e","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2016-06-24T13:10:50Z","title_canon_sha256":"791e8d6633cf909370a3f91f6e3e699be6b945cbc05f503e6f2b018b2a395310"},"schema_version":"1.0","source":{"id":"1606.07674","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1606.07674","created_at":"2026-05-18T01:04:43Z"},{"alias_kind":"arxiv_version","alias_value":"1606.07674v2","created_at":"2026-05-18T01:04:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1606.07674","created_at":"2026-05-18T01:04:43Z"},{"alias_kind":"pith_short_12","alias_value":"F6LCTXLM6ZDP","created_at":"2026-05-18T12:30:15Z"},{"alias_kind":"pith_short_16","alias_value":"F6LCTXLM6ZDP7OIW","created_at":"2026-05-18T12:30:15Z"},{"alias_kind":"pith_short_8","alias_value":"F6LCTXLM","created_at":"2026-05-18T12:30:15Z"}],"graph_snapshots":[{"event_id":"sha256:8dc59b7c6748b59e7b0978601e8e6fc26cef7c589c881e0d188dec276dd316fc","target":"graph","created_at":"2026-05-18T01:04:43Z","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":"This paper proposes implicit CF-NADE, a neural autoregressive model for collaborative filtering tasks using implicit feedback ( e.g. click, watch, browse behaviors). We first convert a users implicit feedback into a like vector and a confidence vector, and then model the probability of the like vector, weighted by the confidence vector. The training objective of implicit CF-NADE is to maximize a weighted negative log-likelihood. We test the performance of implicit CF-NADE on a dataset collected from a popular digital TV streaming service. More specifically, in the experiments, we describe how ","authors_text":"Bangsheng Tang, Cailiang Liu, Hanning Zhou, Yin Zheng","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2016-06-24T13:10:50Z","title":"Neural Autoregressive Collaborative Filtering for Implicit Feedback"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1606.07674","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":"sha256:0046ec22a84e014e4582b5253348eb011620612dfac766c250cce79a4b225278","target":"record","created_at":"2026-05-18T01:04:43Z","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":"a67e4163a9ba0aed9fe8b847dd460a1225acaf1aa055c9c800b74ed3ffe8a17e","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2016-06-24T13:10:50Z","title_canon_sha256":"791e8d6633cf909370a3f91f6e3e699be6b945cbc05f503e6f2b018b2a395310"},"schema_version":"1.0","source":{"id":"1606.07674","kind":"arxiv","version":2}},"canonical_sha256":"2f9629dd6cf646ffb91683af8b2056b2bbff3e1c13f1a68915899f05bf3c1fc9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2f9629dd6cf646ffb91683af8b2056b2bbff3e1c13f1a68915899f05bf3c1fc9","first_computed_at":"2026-05-18T01:04:43.234171Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:04:43.234171Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"C2F7w58mYguO1+HoM9IkMCE/x2DBuwXSDwsjsfnQLYP04lZ1Ds4LxCs4ttiADaYNG/PFC+G3zPspSNynQgyiCw==","signature_status":"signed_v1","signed_at":"2026-05-18T01:04:43.234805Z","signed_message":"canonical_sha256_bytes"},"source_id":"1606.07674","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0046ec22a84e014e4582b5253348eb011620612dfac766c250cce79a4b225278","sha256:8dc59b7c6748b59e7b0978601e8e6fc26cef7c589c881e0d188dec276dd316fc"],"state_sha256":"19a557064f96247c6c4481157faf93eddc58710c64d2995cf59c7ad37416448a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9hVcx9bRFOsDmmAhN0871Dkayv28dXeD9XQQbtcKhXGMBD3CqerKeJ9qag0sxjNuYsBk5xOFh4RL5t/f9lhxCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-31T19:25:58.970754Z","bundle_sha256":"4c3470fa9e2896fa3eed0fd76adbe7300d842123c8d682f3e9bf04fe76eaa12e"}}