{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:O4NW4XHCHK2R2YL5TVV7D5XQY6","short_pith_number":"pith:O4NW4XHC","canonical_record":{"source":{"id":"1907.05559","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2019-07-12T03:11:14Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"4d2ad90dfb4d7d7dd3e39c1154c9f3f75b5d94b9158499656581203cf14414d7","abstract_canon_sha256":"bc476bc9965b6ee004601b61838ca5d63baedcea0cccfd4d68a967fb1001c64b"},"schema_version":"1.0"},"canonical_sha256":"771b6e5ce23ab51d617d9d6bf1f6f0c79d6abad7002b5604d8c5b79d28865d95","source":{"kind":"arxiv","id":"1907.05559","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1907.05559","created_at":"2026-05-17T23:40:47Z"},{"alias_kind":"arxiv_version","alias_value":"1907.05559v1","created_at":"2026-05-17T23:40:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.05559","created_at":"2026-05-17T23:40:47Z"},{"alias_kind":"pith_short_12","alias_value":"O4NW4XHCHK2R","created_at":"2026-05-18T12:33:24Z"},{"alias_kind":"pith_short_16","alias_value":"O4NW4XHCHK2R2YL5","created_at":"2026-05-18T12:33:24Z"},{"alias_kind":"pith_short_8","alias_value":"O4NW4XHC","created_at":"2026-05-18T12:33:24Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:O4NW4XHCHK2R2YL5TVV7D5XQY6","target":"record","payload":{"canonical_record":{"source":{"id":"1907.05559","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2019-07-12T03:11:14Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"4d2ad90dfb4d7d7dd3e39c1154c9f3f75b5d94b9158499656581203cf14414d7","abstract_canon_sha256":"bc476bc9965b6ee004601b61838ca5d63baedcea0cccfd4d68a967fb1001c64b"},"schema_version":"1.0"},"canonical_sha256":"771b6e5ce23ab51d617d9d6bf1f6f0c79d6abad7002b5604d8c5b79d28865d95","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:40:47.942873Z","signature_b64":"tlzOIxycdeoEG37t5Rrj2ifDz4v5AT4ECXlxYeOlvyyAffSuvhQtvCU03FAsMFLhLiDuRiEb+JuEbbK2iuXhBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"771b6e5ce23ab51d617d9d6bf1f6f0c79d6abad7002b5604d8c5b79d28865d95","last_reissued_at":"2026-05-17T23:40:47.942169Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:40:47.942169Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1907.05559","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":"2026-05-17T23:40:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QcgP5dNbOoGVB5whOtTEOuepmLT0OugzDPLvBgZSNlEvtOCCJOhHui3d0cx4YUiMb0dqbZOhjJe2AMfCZ8yLDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T02:17:46.404708Z"},"content_sha256":"b7e5344f4729fd0f4d018cb63e36dc5f95d12219c7b8cbb5a9ee0867404fea36","schema_version":"1.0","event_id":"sha256:b7e5344f4729fd0f4d018cb63e36dc5f95d12219c7b8cbb5a9ee0867404fea36"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:O4NW4XHCHK2R2YL5TVV7D5XQY6","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"NPA: Neural News Recommendation with Personalized Attention","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.IR","authors_text":"Chuhan Wu, Fangzhao Wu, Jianqiang Huang, Mingxiao An, Xing Xie, Yongfeng Huang","submitted_at":"2019-07-12T03:11:14Z","abstract_excerpt":"News recommendation is very important to help users find interested news and alleviate information overload. Different users usually have different interests and the same user may have various interests. Thus, different users may click the same news article with attention on different aspects. In this paper, we propose a neural news recommendation model with personalized attention (NPA). The core of our approach is a news representation model and a user representation model. In the news representation model we use a CNN network to learn hidden representations of news articles based on their ti"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.05559","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":"2026-05-17T23:40:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6ASGXw5gMAcoB2gE52wrUji7miMi/G423wdrszk38crtwrqjfVejEtR/bE1tHPmO9zjYk+CrQcsNVhyTpX/QDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T02:17:46.405250Z"},"content_sha256":"39339324996982229d45372ddca3e582a39cd16d9c4c7ef77eed58124c68e4b4","schema_version":"1.0","event_id":"sha256:39339324996982229d45372ddca3e582a39cd16d9c4c7ef77eed58124c68e4b4"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/O4NW4XHCHK2R2YL5TVV7D5XQY6/bundle.json","state_url":"https://pith.science/pith/O4NW4XHCHK2R2YL5TVV7D5XQY6/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/O4NW4XHCHK2R2YL5TVV7D5XQY6/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":"2026-06-06T02:17:46Z","links":{"resolver":"https://pith.science/pith/O4NW4XHCHK2R2YL5TVV7D5XQY6","bundle":"https://pith.science/pith/O4NW4XHCHK2R2YL5TVV7D5XQY6/bundle.json","state":"https://pith.science/pith/O4NW4XHCHK2R2YL5TVV7D5XQY6/state.json","well_known_bundle":"https://pith.science/.well-known/pith/O4NW4XHCHK2R2YL5TVV7D5XQY6/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:O4NW4XHCHK2R2YL5TVV7D5XQY6","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":"bc476bc9965b6ee004601b61838ca5d63baedcea0cccfd4d68a967fb1001c64b","cross_cats_sorted":["cs.CL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2019-07-12T03:11:14Z","title_canon_sha256":"4d2ad90dfb4d7d7dd3e39c1154c9f3f75b5d94b9158499656581203cf14414d7"},"schema_version":"1.0","source":{"id":"1907.05559","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1907.05559","created_at":"2026-05-17T23:40:47Z"},{"alias_kind":"arxiv_version","alias_value":"1907.05559v1","created_at":"2026-05-17T23:40:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.05559","created_at":"2026-05-17T23:40:47Z"},{"alias_kind":"pith_short_12","alias_value":"O4NW4XHCHK2R","created_at":"2026-05-18T12:33:24Z"},{"alias_kind":"pith_short_16","alias_value":"O4NW4XHCHK2R2YL5","created_at":"2026-05-18T12:33:24Z"},{"alias_kind":"pith_short_8","alias_value":"O4NW4XHC","created_at":"2026-05-18T12:33:24Z"}],"graph_snapshots":[{"event_id":"sha256:39339324996982229d45372ddca3e582a39cd16d9c4c7ef77eed58124c68e4b4","target":"graph","created_at":"2026-05-17T23:40:47Z","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":"News recommendation is very important to help users find interested news and alleviate information overload. Different users usually have different interests and the same user may have various interests. Thus, different users may click the same news article with attention on different aspects. In this paper, we propose a neural news recommendation model with personalized attention (NPA). The core of our approach is a news representation model and a user representation model. In the news representation model we use a CNN network to learn hidden representations of news articles based on their ti","authors_text":"Chuhan Wu, Fangzhao Wu, Jianqiang Huang, Mingxiao An, Xing Xie, Yongfeng Huang","cross_cats":["cs.CL"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2019-07-12T03:11:14Z","title":"NPA: Neural News Recommendation with Personalized Attention"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.05559","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:b7e5344f4729fd0f4d018cb63e36dc5f95d12219c7b8cbb5a9ee0867404fea36","target":"record","created_at":"2026-05-17T23:40:47Z","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":"bc476bc9965b6ee004601b61838ca5d63baedcea0cccfd4d68a967fb1001c64b","cross_cats_sorted":["cs.CL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2019-07-12T03:11:14Z","title_canon_sha256":"4d2ad90dfb4d7d7dd3e39c1154c9f3f75b5d94b9158499656581203cf14414d7"},"schema_version":"1.0","source":{"id":"1907.05559","kind":"arxiv","version":1}},"canonical_sha256":"771b6e5ce23ab51d617d9d6bf1f6f0c79d6abad7002b5604d8c5b79d28865d95","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"771b6e5ce23ab51d617d9d6bf1f6f0c79d6abad7002b5604d8c5b79d28865d95","first_computed_at":"2026-05-17T23:40:47.942169Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:40:47.942169Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"tlzOIxycdeoEG37t5Rrj2ifDz4v5AT4ECXlxYeOlvyyAffSuvhQtvCU03FAsMFLhLiDuRiEb+JuEbbK2iuXhBA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:40:47.942873Z","signed_message":"canonical_sha256_bytes"},"source_id":"1907.05559","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b7e5344f4729fd0f4d018cb63e36dc5f95d12219c7b8cbb5a9ee0867404fea36","sha256:39339324996982229d45372ddca3e582a39cd16d9c4c7ef77eed58124c68e4b4"],"state_sha256":"d5394b02f6b6101320377393533a56d9dae656d3bea3da6116b41ee84ca97bb4"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"74j0Gg8xMecq5/xOonj58XXajigKZemytOlqhqfyz+RD0jn3rjs1xDLFGC/yC44E9sBEg+Yg8HlP5QxKLAipDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-06T02:17:46.407821Z","bundle_sha256":"c321eae3a9f04a5ddbf371f65e1c7429a8f9fa6d67eb35ff208cbc4b82164994"}}