{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:FTGEJQ6R75N7ZDGNYFFOKTTYYH","short_pith_number":"pith:FTGEJQ6R","canonical_record":{"source":{"id":"1807.09751","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2018-07-12T05:06:39Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"432989ac104491599be4e94a49707949de85785482f89d345b16ff553049efd1","abstract_canon_sha256":"0ef5d38fd4aa40b4d68cff9bc7182a60bde04c39f0fb0b951a532ec956a9af91"},"schema_version":"1.0"},"canonical_sha256":"2ccc44c3d1ff5bfc8ccdc14ae54e78c1d8e158f0a7771e61f4d8f284d9668e51","source":{"kind":"arxiv","id":"1807.09751","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1807.09751","created_at":"2026-05-18T00:09:48Z"},{"alias_kind":"arxiv_version","alias_value":"1807.09751v1","created_at":"2026-05-18T00:09:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.09751","created_at":"2026-05-18T00:09:48Z"},{"alias_kind":"pith_short_12","alias_value":"FTGEJQ6R75N7","created_at":"2026-05-18T12:32:25Z"},{"alias_kind":"pith_short_16","alias_value":"FTGEJQ6R75N7ZDGN","created_at":"2026-05-18T12:32:25Z"},{"alias_kind":"pith_short_8","alias_value":"FTGEJQ6R","created_at":"2026-05-18T12:32:25Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:FTGEJQ6R75N7ZDGNYFFOKTTYYH","target":"record","payload":{"canonical_record":{"source":{"id":"1807.09751","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2018-07-12T05:06:39Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"432989ac104491599be4e94a49707949de85785482f89d345b16ff553049efd1","abstract_canon_sha256":"0ef5d38fd4aa40b4d68cff9bc7182a60bde04c39f0fb0b951a532ec956a9af91"},"schema_version":"1.0"},"canonical_sha256":"2ccc44c3d1ff5bfc8ccdc14ae54e78c1d8e158f0a7771e61f4d8f284d9668e51","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:09:48.352598Z","signature_b64":"f/YrRabk67l6rjUzaO9uovH94fd+Cb/9mnlM5X+2PhUP8TzM47TnQkabTwJ8tYr7XPLpnR7FHENbuMtgTlxWCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2ccc44c3d1ff5bfc8ccdc14ae54e78c1d8e158f0a7771e61f4d8f284d9668e51","last_reissued_at":"2026-05-18T00:09:48.351975Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:09:48.351975Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1807.09751","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":"2026-05-18T00:09:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6jNvA5lP22jvIxVQpxTh+frCeHfsgHULob/2jK4PM2SpC8t/ztjhFgxilGdFSGD4r3n+EAquc8wKvuUYk92hCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T05:44:47.232615Z"},"content_sha256":"ef5cc5ec561327fe1f387dc8ea073b14e36b569e9e804ea7f45ff088caaec153","schema_version":"1.0","event_id":"sha256:ef5cc5ec561327fe1f387dc8ea073b14e36b569e9e804ea7f45ff088caaec153"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:FTGEJQ6R75N7ZDGNYFFOKTTYYH","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Multi-Perspective Neural Architecture for Recommendation System","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.IR","authors_text":"Han Xiao, Xiaodong Shi, Yidong Chen","submitted_at":"2018-07-12T05:06:39Z","abstract_excerpt":"Currently, there starts a research trend to leverage neural architecture for recommendation systems. Though several deep recommender models are proposed, most methods are too simple to characterize users' complex preference. In this paper, for a fine-grain analysis, users' ratings are explained from multiple perspectives, based on which, we propose our neural architecture. Specifically, our model employs several sequential stages to encode the user and item into hidden representations. In one stage, the user and item are represented from multiple perspectives and in each perspective, the repre"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.09751","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":"2026-05-18T00:09:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SbB2EYZCG6jWs3QUY0bbydofyIB9JfYvBMMLD3ZNIE9RvpOD6I4CF3qxoFuHE6uetLlau7D1ax9FXDgyhZ2mDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T05:44:47.233066Z"},"content_sha256":"e4a83fb0cd59cd9a84cf9310adf0e70110d822710f7bdfb0f4cd0b4685f1a2ee","schema_version":"1.0","event_id":"sha256:e4a83fb0cd59cd9a84cf9310adf0e70110d822710f7bdfb0f4cd0b4685f1a2ee"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/FTGEJQ6R75N7ZDGNYFFOKTTYYH/bundle.json","state_url":"https://pith.science/pith/FTGEJQ6R75N7ZDGNYFFOKTTYYH/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/FTGEJQ6R75N7ZDGNYFFOKTTYYH/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":"2026-05-31T05:44:47Z","links":{"resolver":"https://pith.science/pith/FTGEJQ6R75N7ZDGNYFFOKTTYYH","bundle":"https://pith.science/pith/FTGEJQ6R75N7ZDGNYFFOKTTYYH/bundle.json","state":"https://pith.science/pith/FTGEJQ6R75N7ZDGNYFFOKTTYYH/state.json","well_known_bundle":"https://pith.science/.well-known/pith/FTGEJQ6R75N7ZDGNYFFOKTTYYH/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:FTGEJQ6R75N7ZDGNYFFOKTTYYH","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":"0ef5d38fd4aa40b4d68cff9bc7182a60bde04c39f0fb0b951a532ec956a9af91","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2018-07-12T05:06:39Z","title_canon_sha256":"432989ac104491599be4e94a49707949de85785482f89d345b16ff553049efd1"},"schema_version":"1.0","source":{"id":"1807.09751","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1807.09751","created_at":"2026-05-18T00:09:48Z"},{"alias_kind":"arxiv_version","alias_value":"1807.09751v1","created_at":"2026-05-18T00:09:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.09751","created_at":"2026-05-18T00:09:48Z"},{"alias_kind":"pith_short_12","alias_value":"FTGEJQ6R75N7","created_at":"2026-05-18T12:32:25Z"},{"alias_kind":"pith_short_16","alias_value":"FTGEJQ6R75N7ZDGN","created_at":"2026-05-18T12:32:25Z"},{"alias_kind":"pith_short_8","alias_value":"FTGEJQ6R","created_at":"2026-05-18T12:32:25Z"}],"graph_snapshots":[{"event_id":"sha256:e4a83fb0cd59cd9a84cf9310adf0e70110d822710f7bdfb0f4cd0b4685f1a2ee","target":"graph","created_at":"2026-05-18T00:09:48Z","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":"Currently, there starts a research trend to leverage neural architecture for recommendation systems. Though several deep recommender models are proposed, most methods are too simple to characterize users' complex preference. In this paper, for a fine-grain analysis, users' ratings are explained from multiple perspectives, based on which, we propose our neural architecture. Specifically, our model employs several sequential stages to encode the user and item into hidden representations. In one stage, the user and item are represented from multiple perspectives and in each perspective, the repre","authors_text":"Han Xiao, Xiaodong Shi, Yidong Chen","cross_cats":["cs.LG","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2018-07-12T05:06:39Z","title":"Multi-Perspective Neural Architecture for Recommendation System"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.09751","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:ef5cc5ec561327fe1f387dc8ea073b14e36b569e9e804ea7f45ff088caaec153","target":"record","created_at":"2026-05-18T00:09:48Z","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":"0ef5d38fd4aa40b4d68cff9bc7182a60bde04c39f0fb0b951a532ec956a9af91","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2018-07-12T05:06:39Z","title_canon_sha256":"432989ac104491599be4e94a49707949de85785482f89d345b16ff553049efd1"},"schema_version":"1.0","source":{"id":"1807.09751","kind":"arxiv","version":1}},"canonical_sha256":"2ccc44c3d1ff5bfc8ccdc14ae54e78c1d8e158f0a7771e61f4d8f284d9668e51","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2ccc44c3d1ff5bfc8ccdc14ae54e78c1d8e158f0a7771e61f4d8f284d9668e51","first_computed_at":"2026-05-18T00:09:48.351975Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:09:48.351975Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"f/YrRabk67l6rjUzaO9uovH94fd+Cb/9mnlM5X+2PhUP8TzM47TnQkabTwJ8tYr7XPLpnR7FHENbuMtgTlxWCg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:09:48.352598Z","signed_message":"canonical_sha256_bytes"},"source_id":"1807.09751","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ef5cc5ec561327fe1f387dc8ea073b14e36b569e9e804ea7f45ff088caaec153","sha256:e4a83fb0cd59cd9a84cf9310adf0e70110d822710f7bdfb0f4cd0b4685f1a2ee"],"state_sha256":"5a61b01fbe48ab147931ef3767a63b1c658e7f115c9f98db69eed922114989be"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iaGTnO1s/RAEwk6MjJ8UdlgV2YttHymX6yyn127vxSvcV5dT3HAwCp8EjOX2QpSCGr+7472v/bqwWLomsVCIDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-31T05:44:47.238723Z","bundle_sha256":"6510574d956865938aef6c5d1d4526e83d897578a3ba696d765bcefb70cf0517"}}