{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:RKZOPKENRNOGXVUZEPXDJPELXD","short_pith_number":"pith:RKZOPKEN","schema_version":"1.0","canonical_sha256":"8ab2e7a88d8b5c6bd69923ee34bc8bb8d3cbc0880c5eadba64ba887989674eb0","source":{"kind":"arxiv","id":"1809.07053","version":1},"attestation_state":"computed","paper":{"title":"NAIS: Neural Attentive Item Similarity Model for Recommendation","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Jingkuan Song, Tat-Seng Chua, Xiangnan He, Yu-Gang Jiang, Zhankui He, Zhenguang Liu","submitted_at":"2018-09-19T08:17:54Z","abstract_excerpt":"Item-to-item collaborative filtering (aka. item-based CF) has been long used for building recommender systems in industrial settings, owing to its interpretability and efficiency in real-time personalization. It builds a user's profile as her historically interacted items, recommending new items that are similar to the user's profile. As such, the key to an item-based CF method is in the estimation of item similarities. Early approaches use statistical measures such as cosine similarity and Pearson coefficient to estimate item similarities, which are less accurate since they lack tailored opti"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1809.07053","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.IR","submitted_at":"2018-09-19T08:17:54Z","cross_cats_sorted":[],"title_canon_sha256":"389fcf9562273aa5c6fbad3504d032c037f310bb2e573d1722c807f4d86f0d1f","abstract_canon_sha256":"a7d950a3b73252299e08d2d2d82b50a9a6bcf3d81c86986bc34d2338de51f3f4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:05:20.802344Z","signature_b64":"nXzB2XA5H3dUtPocjAeEw3xyf/ovyeBhcaRNK3frDX5x+oC8Taf/zksrtxo5TS4Ngne0bfqvtMJeqJAqkq1pCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8ab2e7a88d8b5c6bd69923ee34bc8bb8d3cbc0880c5eadba64ba887989674eb0","last_reissued_at":"2026-05-18T00:05:20.801743Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:05:20.801743Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"NAIS: Neural Attentive Item Similarity Model for Recommendation","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Jingkuan Song, Tat-Seng Chua, Xiangnan He, Yu-Gang Jiang, Zhankui He, Zhenguang Liu","submitted_at":"2018-09-19T08:17:54Z","abstract_excerpt":"Item-to-item collaborative filtering (aka. item-based CF) has been long used for building recommender systems in industrial settings, owing to its interpretability and efficiency in real-time personalization. It builds a user's profile as her historically interacted items, recommending new items that are similar to the user's profile. As such, the key to an item-based CF method is in the estimation of item similarities. Early approaches use statistical measures such as cosine similarity and Pearson coefficient to estimate item similarities, which are less accurate since they lack tailored opti"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.07053","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1809.07053","created_at":"2026-05-18T00:05:20.801842+00:00"},{"alias_kind":"arxiv_version","alias_value":"1809.07053v1","created_at":"2026-05-18T00:05:20.801842+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.07053","created_at":"2026-05-18T00:05:20.801842+00:00"},{"alias_kind":"pith_short_12","alias_value":"RKZOPKENRNOG","created_at":"2026-05-18T12:32:50.500415+00:00"},{"alias_kind":"pith_short_16","alias_value":"RKZOPKENRNOGXVUZ","created_at":"2026-05-18T12:32:50.500415+00:00"},{"alias_kind":"pith_short_8","alias_value":"RKZOPKEN","created_at":"2026-05-18T12:32:50.500415+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/RKZOPKENRNOGXVUZEPXDJPELXD","json":"https://pith.science/pith/RKZOPKENRNOGXVUZEPXDJPELXD.json","graph_json":"https://pith.science/api/pith-number/RKZOPKENRNOGXVUZEPXDJPELXD/graph.json","events_json":"https://pith.science/api/pith-number/RKZOPKENRNOGXVUZEPXDJPELXD/events.json","paper":"https://pith.science/paper/RKZOPKEN"},"agent_actions":{"view_html":"https://pith.science/pith/RKZOPKENRNOGXVUZEPXDJPELXD","download_json":"https://pith.science/pith/RKZOPKENRNOGXVUZEPXDJPELXD.json","view_paper":"https://pith.science/paper/RKZOPKEN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1809.07053&json=true","fetch_graph":"https://pith.science/api/pith-number/RKZOPKENRNOGXVUZEPXDJPELXD/graph.json","fetch_events":"https://pith.science/api/pith-number/RKZOPKENRNOGXVUZEPXDJPELXD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RKZOPKENRNOGXVUZEPXDJPELXD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RKZOPKENRNOGXVUZEPXDJPELXD/action/storage_attestation","attest_author":"https://pith.science/pith/RKZOPKENRNOGXVUZEPXDJPELXD/action/author_attestation","sign_citation":"https://pith.science/pith/RKZOPKENRNOGXVUZEPXDJPELXD/action/citation_signature","submit_replication":"https://pith.science/pith/RKZOPKENRNOGXVUZEPXDJPELXD/action/replication_record"}},"created_at":"2026-05-18T00:05:20.801842+00:00","updated_at":"2026-05-18T00:05:20.801842+00:00"}