{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:FQSN2VQ7LO6I53QTEVOO63WDVU","short_pith_number":"pith:FQSN2VQ7","canonical_record":{"source":{"id":"1607.04373","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2016-07-15T03:35:56Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"1091f058ffabbca3c838323d61de6ffb1baf52e0adf96f89230340b1dfcdd2e2","abstract_canon_sha256":"1a13ea315797fac412888bd39ea030d5f163e1485fc6913a3e39e4f46baba5ec"},"schema_version":"1.0"},"canonical_sha256":"2c24dd561f5bbc8eee13255cef6ec3ad34f66cc60d88373f188344100fdf788e","source":{"kind":"arxiv","id":"1607.04373","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1607.04373","created_at":"2026-05-18T01:11:01Z"},{"alias_kind":"arxiv_version","alias_value":"1607.04373v1","created_at":"2026-05-18T01:11:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1607.04373","created_at":"2026-05-18T01:11:01Z"},{"alias_kind":"pith_short_12","alias_value":"FQSN2VQ7LO6I","created_at":"2026-05-18T12:30:15Z"},{"alias_kind":"pith_short_16","alias_value":"FQSN2VQ7LO6I53QT","created_at":"2026-05-18T12:30:15Z"},{"alias_kind":"pith_short_8","alias_value":"FQSN2VQ7","created_at":"2026-05-18T12:30:15Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:FQSN2VQ7LO6I53QTEVOO63WDVU","target":"record","payload":{"canonical_record":{"source":{"id":"1607.04373","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2016-07-15T03:35:56Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"1091f058ffabbca3c838323d61de6ffb1baf52e0adf96f89230340b1dfcdd2e2","abstract_canon_sha256":"1a13ea315797fac412888bd39ea030d5f163e1485fc6913a3e39e4f46baba5ec"},"schema_version":"1.0"},"canonical_sha256":"2c24dd561f5bbc8eee13255cef6ec3ad34f66cc60d88373f188344100fdf788e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:11:01.396872Z","signature_b64":"W18cbGa2yaLGcmKIQUSdUg8TNzHOajIktHc7MtimE4EjNLDlBh16BP6BDr20CvmE+CkqzKJtF6xT/ldH61aKCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2c24dd561f5bbc8eee13255cef6ec3ad34f66cc60d88373f188344100fdf788e","last_reissued_at":"2026-05-18T01:11:01.396135Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:11:01.396135Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1607.04373","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":"2026-05-18T01:11:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UX8UWTCODFhBVXJgIaNRC3Ffj4VYCWVbW7zbfpmyd/tQXRHne7C/3Mh0jUeoaPWbvzLyu0vGoHJM8MNHTdiKDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T07:34:45.479988Z"},"content_sha256":"919bf5ad1befc25475c613b3c86a5cd09f1b390b936fdbcf76d76fe44bb61698","schema_version":"1.0","event_id":"sha256:919bf5ad1befc25475c613b3c86a5cd09f1b390b936fdbcf76d76fe44bb61698"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:FQSN2VQ7LO6I53QTEVOO63WDVU","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Vista: A Visually, Socially, and Temporally-aware Model for Artistic Recommendation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.IR","authors_text":"Chen Fang, Julian McAuley, Ruining He, Zhaowen Wang","submitted_at":"2016-07-15T03:35:56Z","abstract_excerpt":"Understanding users' interactions with highly subjective content---like artistic images---is challenging due to the complex semantics that guide our preferences. On the one hand one has to overcome `standard' recommender systems challenges, such as dealing with large, sparse, and long-tailed datasets. On the other, several new challenges present themselves, such as the need to model content in terms of its visual appearance, or even social dynamics, such as a preference toward a particular artist that is independent of the art they create.\n  In this paper we build large-scale recommender syste"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1607.04373","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":"2026-05-18T01:11:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qomD4kZURdWruT87OIIa8eVRxWhyLROYvYpYO5q+DfNHEtJmc9DxbSxD1DD3MQVavhircEtN/Gno9ukIb5R3DQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T07:34:45.480691Z"},"content_sha256":"feff5d13c083bb61ca6773f64d106386a5fc8d87c4448584ceb70456cd312077","schema_version":"1.0","event_id":"sha256:feff5d13c083bb61ca6773f64d106386a5fc8d87c4448584ceb70456cd312077"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/FQSN2VQ7LO6I53QTEVOO63WDVU/bundle.json","state_url":"https://pith.science/pith/FQSN2VQ7LO6I53QTEVOO63WDVU/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/FQSN2VQ7LO6I53QTEVOO63WDVU/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":"2026-05-31T07:34:45Z","links":{"resolver":"https://pith.science/pith/FQSN2VQ7LO6I53QTEVOO63WDVU","bundle":"https://pith.science/pith/FQSN2VQ7LO6I53QTEVOO63WDVU/bundle.json","state":"https://pith.science/pith/FQSN2VQ7LO6I53QTEVOO63WDVU/state.json","well_known_bundle":"https://pith.science/.well-known/pith/FQSN2VQ7LO6I53QTEVOO63WDVU/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:FQSN2VQ7LO6I53QTEVOO63WDVU","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":"1a13ea315797fac412888bd39ea030d5f163e1485fc6913a3e39e4f46baba5ec","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2016-07-15T03:35:56Z","title_canon_sha256":"1091f058ffabbca3c838323d61de6ffb1baf52e0adf96f89230340b1dfcdd2e2"},"schema_version":"1.0","source":{"id":"1607.04373","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1607.04373","created_at":"2026-05-18T01:11:01Z"},{"alias_kind":"arxiv_version","alias_value":"1607.04373v1","created_at":"2026-05-18T01:11:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1607.04373","created_at":"2026-05-18T01:11:01Z"},{"alias_kind":"pith_short_12","alias_value":"FQSN2VQ7LO6I","created_at":"2026-05-18T12:30:15Z"},{"alias_kind":"pith_short_16","alias_value":"FQSN2VQ7LO6I53QT","created_at":"2026-05-18T12:30:15Z"},{"alias_kind":"pith_short_8","alias_value":"FQSN2VQ7","created_at":"2026-05-18T12:30:15Z"}],"graph_snapshots":[{"event_id":"sha256:feff5d13c083bb61ca6773f64d106386a5fc8d87c4448584ceb70456cd312077","target":"graph","created_at":"2026-05-18T01:11:01Z","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":"Understanding users' interactions with highly subjective content---like artistic images---is challenging due to the complex semantics that guide our preferences. On the one hand one has to overcome `standard' recommender systems challenges, such as dealing with large, sparse, and long-tailed datasets. On the other, several new challenges present themselves, such as the need to model content in terms of its visual appearance, or even social dynamics, such as a preference toward a particular artist that is independent of the art they create.\n  In this paper we build large-scale recommender syste","authors_text":"Chen Fang, Julian McAuley, Ruining He, Zhaowen Wang","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2016-07-15T03:35:56Z","title":"Vista: A Visually, Socially, and Temporally-aware Model for Artistic Recommendation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1607.04373","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":"sha256:919bf5ad1befc25475c613b3c86a5cd09f1b390b936fdbcf76d76fe44bb61698","target":"record","created_at":"2026-05-18T01:11:01Z","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":"1a13ea315797fac412888bd39ea030d5f163e1485fc6913a3e39e4f46baba5ec","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2016-07-15T03:35:56Z","title_canon_sha256":"1091f058ffabbca3c838323d61de6ffb1baf52e0adf96f89230340b1dfcdd2e2"},"schema_version":"1.0","source":{"id":"1607.04373","kind":"arxiv","version":1}},"canonical_sha256":"2c24dd561f5bbc8eee13255cef6ec3ad34f66cc60d88373f188344100fdf788e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2c24dd561f5bbc8eee13255cef6ec3ad34f66cc60d88373f188344100fdf788e","first_computed_at":"2026-05-18T01:11:01.396135Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:11:01.396135Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"W18cbGa2yaLGcmKIQUSdUg8TNzHOajIktHc7MtimE4EjNLDlBh16BP6BDr20CvmE+CkqzKJtF6xT/ldH61aKCA==","signature_status":"signed_v1","signed_at":"2026-05-18T01:11:01.396872Z","signed_message":"canonical_sha256_bytes"},"source_id":"1607.04373","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:919bf5ad1befc25475c613b3c86a5cd09f1b390b936fdbcf76d76fe44bb61698","sha256:feff5d13c083bb61ca6773f64d106386a5fc8d87c4448584ceb70456cd312077"],"state_sha256":"a04c16ae8b501df2b589975206e1f1841cd475f0dd042a903728af7e34161182"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YEVYW5sfqvJnGfpgBBsiTDEkLgttwPj0dd6uI5X3NP/Mg6JUzHB2GGCjGyElikv+UIYRrkXSFGD7HOiR5pKyCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-31T07:34:45.484709Z","bundle_sha256":"a692828d8984326a98a9d72b9ac5693bd83088a3181edff8821cd1dbeaffd51a"}}