{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:CVU4775NDIK72SGN55T7W3XTRX","short_pith_number":"pith:CVU4775N","canonical_record":{"source":{"id":"1712.04350","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-12-11T18:54:23Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"857d371c9b28ecbd3e3fbddc1a840df2cdf4bf1dbe89ef83f83bb6e2d80d0c86","abstract_canon_sha256":"ab1cafc0767dd67c88cfbfad5a361a713c940b613f030d07211295d5234c19cf"},"schema_version":"1.0"},"canonical_sha256":"1569cfffad1a15fd48cdef67fb6ef38dfe2a8179af09895e889be4ae34105cd8","source":{"kind":"arxiv","id":"1712.04350","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1712.04350","created_at":"2026-05-18T00:28:08Z"},{"alias_kind":"arxiv_version","alias_value":"1712.04350v1","created_at":"2026-05-18T00:28:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1712.04350","created_at":"2026-05-18T00:28:08Z"},{"alias_kind":"pith_short_12","alias_value":"CVU4775NDIK7","created_at":"2026-05-18T12:31:10Z"},{"alias_kind":"pith_short_16","alias_value":"CVU4775NDIK72SGN","created_at":"2026-05-18T12:31:10Z"},{"alias_kind":"pith_short_8","alias_value":"CVU4775N","created_at":"2026-05-18T12:31:10Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:CVU4775NDIK72SGN55T7W3XTRX","target":"record","payload":{"canonical_record":{"source":{"id":"1712.04350","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-12-11T18:54:23Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"857d371c9b28ecbd3e3fbddc1a840df2cdf4bf1dbe89ef83f83bb6e2d80d0c86","abstract_canon_sha256":"ab1cafc0767dd67c88cfbfad5a361a713c940b613f030d07211295d5234c19cf"},"schema_version":"1.0"},"canonical_sha256":"1569cfffad1a15fd48cdef67fb6ef38dfe2a8179af09895e889be4ae34105cd8","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:28:08.250208Z","signature_b64":"gG1MlhPGpEjVO9hvOdrWxxQho1YTAaqA2waF8NS64jN0xU6cFp0mqPN3+Vipyia4b3Id0vpxCLyABi24zVytDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1569cfffad1a15fd48cdef67fb6ef38dfe2a8179af09895e889be4ae34105cd8","last_reissued_at":"2026-05-18T00:28:08.249491Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:28:08.249491Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1712.04350","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":"2026-05-18T00:28:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8xC2d0TP/V4fUDNN3LbiHT3I9FCRwgw1Uqk/9ck1usEaqg0b5l6T08CjM3qeDwCaCpc9Ol5uDLs2RrrwBaZcCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T06:23:25.147004Z"},"content_sha256":"328a81d5b30d7fbe25be7fe91a62a06083a9686d50ec783146818dddcaaf3c94","schema_version":"1.0","event_id":"sha256:328a81d5b30d7fbe25be7fe91a62a06083a9686d50ec783146818dddcaaf3c94"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:CVU4775NDIK72SGN55T7W3XTRX","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Predicting Yelp Star Reviews Based on Network Structure with Deep Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Luis Perez","submitted_at":"2017-12-11T18:54:23Z","abstract_excerpt":"In this paper, we tackle the real-world problem of predicting Yelp star-review rating based on business features (such as images, descriptions), user features (average previous ratings), and, of particular interest, network properties (which businesses has a user rated before). We compare multiple models on different sets of features -- from simple linear regression on network features only to deep learning models on network and item features.\n  In recent years, breakthroughs in deep learning have led to increased accuracy in common supervised learning tasks, such as image classification, capt"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1712.04350","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":"2026-05-18T00:28:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tyaut7Q8BYsCOvQMvFAdWt/QeHngCIv8ITYg3qVxolO6gkcOHh80rCCy40dygiekjrT8a2PeFYsrjsgvOPb2DQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T06:23:25.147704Z"},"content_sha256":"bcf0143e4c00037890999f611994c845a9769248d4064048cae92d4ff888a933","schema_version":"1.0","event_id":"sha256:bcf0143e4c00037890999f611994c845a9769248d4064048cae92d4ff888a933"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/CVU4775NDIK72SGN55T7W3XTRX/bundle.json","state_url":"https://pith.science/pith/CVU4775NDIK72SGN55T7W3XTRX/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/CVU4775NDIK72SGN55T7W3XTRX/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":"2026-06-06T06:23:25Z","links":{"resolver":"https://pith.science/pith/CVU4775NDIK72SGN55T7W3XTRX","bundle":"https://pith.science/pith/CVU4775NDIK72SGN55T7W3XTRX/bundle.json","state":"https://pith.science/pith/CVU4775NDIK72SGN55T7W3XTRX/state.json","well_known_bundle":"https://pith.science/.well-known/pith/CVU4775NDIK72SGN55T7W3XTRX/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:CVU4775NDIK72SGN55T7W3XTRX","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":"ab1cafc0767dd67c88cfbfad5a361a713c940b613f030d07211295d5234c19cf","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-12-11T18:54:23Z","title_canon_sha256":"857d371c9b28ecbd3e3fbddc1a840df2cdf4bf1dbe89ef83f83bb6e2d80d0c86"},"schema_version":"1.0","source":{"id":"1712.04350","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1712.04350","created_at":"2026-05-18T00:28:08Z"},{"alias_kind":"arxiv_version","alias_value":"1712.04350v1","created_at":"2026-05-18T00:28:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1712.04350","created_at":"2026-05-18T00:28:08Z"},{"alias_kind":"pith_short_12","alias_value":"CVU4775NDIK7","created_at":"2026-05-18T12:31:10Z"},{"alias_kind":"pith_short_16","alias_value":"CVU4775NDIK72SGN","created_at":"2026-05-18T12:31:10Z"},{"alias_kind":"pith_short_8","alias_value":"CVU4775N","created_at":"2026-05-18T12:31:10Z"}],"graph_snapshots":[{"event_id":"sha256:bcf0143e4c00037890999f611994c845a9769248d4064048cae92d4ff888a933","target":"graph","created_at":"2026-05-18T00:28:08Z","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":"In this paper, we tackle the real-world problem of predicting Yelp star-review rating based on business features (such as images, descriptions), user features (average previous ratings), and, of particular interest, network properties (which businesses has a user rated before). We compare multiple models on different sets of features -- from simple linear regression on network features only to deep learning models on network and item features.\n  In recent years, breakthroughs in deep learning have led to increased accuracy in common supervised learning tasks, such as image classification, capt","authors_text":"Luis Perez","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-12-11T18:54:23Z","title":"Predicting Yelp Star Reviews Based on Network Structure with Deep Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1712.04350","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":"sha256:328a81d5b30d7fbe25be7fe91a62a06083a9686d50ec783146818dddcaaf3c94","target":"record","created_at":"2026-05-18T00:28:08Z","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":"ab1cafc0767dd67c88cfbfad5a361a713c940b613f030d07211295d5234c19cf","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-12-11T18:54:23Z","title_canon_sha256":"857d371c9b28ecbd3e3fbddc1a840df2cdf4bf1dbe89ef83f83bb6e2d80d0c86"},"schema_version":"1.0","source":{"id":"1712.04350","kind":"arxiv","version":1}},"canonical_sha256":"1569cfffad1a15fd48cdef67fb6ef38dfe2a8179af09895e889be4ae34105cd8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1569cfffad1a15fd48cdef67fb6ef38dfe2a8179af09895e889be4ae34105cd8","first_computed_at":"2026-05-18T00:28:08.249491Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:28:08.249491Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"gG1MlhPGpEjVO9hvOdrWxxQho1YTAaqA2waF8NS64jN0xU6cFp0mqPN3+Vipyia4b3Id0vpxCLyABi24zVytDg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:28:08.250208Z","signed_message":"canonical_sha256_bytes"},"source_id":"1712.04350","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:328a81d5b30d7fbe25be7fe91a62a06083a9686d50ec783146818dddcaaf3c94","sha256:bcf0143e4c00037890999f611994c845a9769248d4064048cae92d4ff888a933"],"state_sha256":"c39e535611becc33dfea1bbbbfd189ef23dce60ad13feb1923ee434dd07c142e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LtJnwPp27JzPuziNElz6PzGsf6irxVKcc7wF8ACuXjFrbNu1cYMdYWnCnUjegApxvgKNufFjvX6MRDoj8YsQDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-06T06:23:25.151492Z","bundle_sha256":"9307790ba38c462b17ccdc65fe571963feaea1751f22f0e8dfb3df1a18fb8cbc"}}