{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:YZBMEVN42HNCYG7Y224CHRW7UE","short_pith_number":"pith:YZBMEVN4","canonical_record":{"source":{"id":"1807.05730","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2018-07-16T08:45:44Z","cross_cats_sorted":[],"title_canon_sha256":"745aa492e35e3ebb4d5eb4432904ea276833b1c380bd502f249eef69e8ef2f1c","abstract_canon_sha256":"1884611e3e38115faf35495b62066f5c9f278be72ffc0af71ef3e89b7c691405"},"schema_version":"1.0"},"canonical_sha256":"c642c255bcd1da2c1bf8d6b823c6dfa11ed5e2e34de65402b776398f51f47e22","source":{"kind":"arxiv","id":"1807.05730","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1807.05730","created_at":"2026-05-18T00:10:42Z"},{"alias_kind":"arxiv_version","alias_value":"1807.05730v1","created_at":"2026-05-18T00:10:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.05730","created_at":"2026-05-18T00:10:42Z"},{"alias_kind":"pith_short_12","alias_value":"YZBMEVN42HNC","created_at":"2026-05-18T12:33:04Z"},{"alias_kind":"pith_short_16","alias_value":"YZBMEVN42HNCYG7Y","created_at":"2026-05-18T12:33:04Z"},{"alias_kind":"pith_short_8","alias_value":"YZBMEVN4","created_at":"2026-05-18T12:33:04Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:YZBMEVN42HNCYG7Y224CHRW7UE","target":"record","payload":{"canonical_record":{"source":{"id":"1807.05730","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2018-07-16T08:45:44Z","cross_cats_sorted":[],"title_canon_sha256":"745aa492e35e3ebb4d5eb4432904ea276833b1c380bd502f249eef69e8ef2f1c","abstract_canon_sha256":"1884611e3e38115faf35495b62066f5c9f278be72ffc0af71ef3e89b7c691405"},"schema_version":"1.0"},"canonical_sha256":"c642c255bcd1da2c1bf8d6b823c6dfa11ed5e2e34de65402b776398f51f47e22","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:10:42.722464Z","signature_b64":"np5dcBurrGqEWs6kkZUXwwt3i2sxaBuj7QKx3g1GSh3c38JSagP5Sw0OAYeSPEpt8xEPA9bpUHICqPGKg8AfAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c642c255bcd1da2c1bf8d6b823c6dfa11ed5e2e34de65402b776398f51f47e22","last_reissued_at":"2026-05-18T00:10:42.721827Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:10:42.721827Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1807.05730","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":"2026-05-18T00:10:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GPlcGlF4Xb1f6vQZv4CtzXyg4Kw3KyIGSQ2FMsfFCHRN3Evnmf/l94hEJVSYZCb1EvgO3TxqdjAzLIQnPk6xAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T02:28:06.853718Z"},"content_sha256":"476aa42bdaa53186bb072f93610bfb2ab89cd216b2b86a9f92aadc70d6c2e3ff","schema_version":"1.0","event_id":"sha256:476aa42bdaa53186bb072f93610bfb2ab89cd216b2b86a9f92aadc70d6c2e3ff"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:YZBMEVN42HNCYG7Y224CHRW7UE","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Collective Variational Autoencoder for Top-$N$ Recommendation with Side Information","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Maarten de Rijke, Yifan Chen","submitted_at":"2018-07-16T08:45:44Z","abstract_excerpt":"Recommender systems have been studied extensively due to their practical use in many real-world scenarios. Despite this, generating effective recommendations with sparse user ratings remains a challenge. Side information associated with items has been widely utilized to address rating sparsity. Existing recommendation models that use side information are linear and, hence, have restricted expressiveness. Deep learning has been used to capture non-linearities by learning deep item representations from side information but as side information is high-dimensional existing deep models tend to have"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.05730","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":"2026-05-18T00:10:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Tc3umCiGNoM6Fw7LXUWLJOI7TMxKEoOwSi8eEOlVuFhpWD9AvaVJhnIXJqzGFSztzbygmKLnWAR7US2VXhL9AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T02:28:06.854170Z"},"content_sha256":"0be78dc48a42295a20c8c9e799b25cfd8198abed33a9f6abbd59aa7f1cf16171","schema_version":"1.0","event_id":"sha256:0be78dc48a42295a20c8c9e799b25cfd8198abed33a9f6abbd59aa7f1cf16171"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YZBMEVN42HNCYG7Y224CHRW7UE/bundle.json","state_url":"https://pith.science/pith/YZBMEVN42HNCYG7Y224CHRW7UE/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YZBMEVN42HNCYG7Y224CHRW7UE/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":"2026-05-26T02:28:06Z","links":{"resolver":"https://pith.science/pith/YZBMEVN42HNCYG7Y224CHRW7UE","bundle":"https://pith.science/pith/YZBMEVN42HNCYG7Y224CHRW7UE/bundle.json","state":"https://pith.science/pith/YZBMEVN42HNCYG7Y224CHRW7UE/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YZBMEVN42HNCYG7Y224CHRW7UE/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:YZBMEVN42HNCYG7Y224CHRW7UE","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":"1884611e3e38115faf35495b62066f5c9f278be72ffc0af71ef3e89b7c691405","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2018-07-16T08:45:44Z","title_canon_sha256":"745aa492e35e3ebb4d5eb4432904ea276833b1c380bd502f249eef69e8ef2f1c"},"schema_version":"1.0","source":{"id":"1807.05730","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1807.05730","created_at":"2026-05-18T00:10:42Z"},{"alias_kind":"arxiv_version","alias_value":"1807.05730v1","created_at":"2026-05-18T00:10:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.05730","created_at":"2026-05-18T00:10:42Z"},{"alias_kind":"pith_short_12","alias_value":"YZBMEVN42HNC","created_at":"2026-05-18T12:33:04Z"},{"alias_kind":"pith_short_16","alias_value":"YZBMEVN42HNCYG7Y","created_at":"2026-05-18T12:33:04Z"},{"alias_kind":"pith_short_8","alias_value":"YZBMEVN4","created_at":"2026-05-18T12:33:04Z"}],"graph_snapshots":[{"event_id":"sha256:0be78dc48a42295a20c8c9e799b25cfd8198abed33a9f6abbd59aa7f1cf16171","target":"graph","created_at":"2026-05-18T00:10:42Z","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":"Recommender systems have been studied extensively due to their practical use in many real-world scenarios. Despite this, generating effective recommendations with sparse user ratings remains a challenge. Side information associated with items has been widely utilized to address rating sparsity. Existing recommendation models that use side information are linear and, hence, have restricted expressiveness. Deep learning has been used to capture non-linearities by learning deep item representations from side information but as side information is high-dimensional existing deep models tend to have","authors_text":"Maarten de Rijke, Yifan Chen","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2018-07-16T08:45:44Z","title":"A Collective Variational Autoencoder for Top-$N$ Recommendation with Side Information"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.05730","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":"sha256:476aa42bdaa53186bb072f93610bfb2ab89cd216b2b86a9f92aadc70d6c2e3ff","target":"record","created_at":"2026-05-18T00:10:42Z","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":"1884611e3e38115faf35495b62066f5c9f278be72ffc0af71ef3e89b7c691405","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2018-07-16T08:45:44Z","title_canon_sha256":"745aa492e35e3ebb4d5eb4432904ea276833b1c380bd502f249eef69e8ef2f1c"},"schema_version":"1.0","source":{"id":"1807.05730","kind":"arxiv","version":1}},"canonical_sha256":"c642c255bcd1da2c1bf8d6b823c6dfa11ed5e2e34de65402b776398f51f47e22","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c642c255bcd1da2c1bf8d6b823c6dfa11ed5e2e34de65402b776398f51f47e22","first_computed_at":"2026-05-18T00:10:42.721827Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:10:42.721827Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"np5dcBurrGqEWs6kkZUXwwt3i2sxaBuj7QKx3g1GSh3c38JSagP5Sw0OAYeSPEpt8xEPA9bpUHICqPGKg8AfAQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:10:42.722464Z","signed_message":"canonical_sha256_bytes"},"source_id":"1807.05730","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:476aa42bdaa53186bb072f93610bfb2ab89cd216b2b86a9f92aadc70d6c2e3ff","sha256:0be78dc48a42295a20c8c9e799b25cfd8198abed33a9f6abbd59aa7f1cf16171"],"state_sha256":"749dabb8e693b018a22d0888f61dfabe094594a14fb465d629b254289706cec9"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ogZmbpFsbv3TvsC/c/HR7AI/RjwYPgtE8/7LbpMu8oClfk0UUUaIVM6iktm3dSp0Yb4YPH9bjbDVNwD91PfADg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T02:28:06.856394Z","bundle_sha256":"d0d5fa7224d1a1a6085270430961685208359331769629c81e95a48c0e28ca05"}}