{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:Q57WZY2EBP747WBKHUHPWWCC25","short_pith_number":"pith:Q57WZY2E","schema_version":"1.0","canonical_sha256":"877f6ce3440bffcfd82a3d0efb5842d74aa0b0fac9fac8134750af8925e288f2","source":{"kind":"arxiv","id":"2102.07619","version":2},"attestation_state":"computed","paper":{"title":"MaskNet: Introducing Feature-Wise Multiplication to CTR Ranking Models by Instance-Guided Mask","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Junlin Zhang, Qingyun She, Zhiqiang Wang","submitted_at":"2021-02-09T12:27:49Z","abstract_excerpt":"Click-Through Rate(CTR) estimation has become one of the most fundamental tasks in many real-world applications and it's important for ranking models to effectively capture complex high-order features. Shallow feed-forward network is widely used in many state-of-the-art DNN models such as FNN, DeepFM and xDeepFM to implicitly capture high-order feature interactions. However, some research has proved that addictive feature interaction, particular feed-forward neural networks, is inefficient in capturing common feature interaction. To resolve this problem, we introduce specific multiplicative op"},"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":"2102.07619","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2021-02-09T12:27:49Z","cross_cats_sorted":[],"title_canon_sha256":"236cd5ffb673fd1a7dfec2cc535873989d77da6a4df46218b5e088fe18dd2840","abstract_canon_sha256":"8139f26507b0fabd3e808a4c88ef4ade1dd42e99c89969e0f66d27e73dfcd6f0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:00:31.769251Z","signature_b64":"R3cwIjmq9RnPSd1zujXhzFwE3dsojSSGogkWOhzvB5GMcl5tLilT3VzIEjjHZoJs2289uza90SIKABKsvLBNCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"877f6ce3440bffcfd82a3d0efb5842d74aa0b0fac9fac8134750af8925e288f2","last_reissued_at":"2026-07-05T03:00:31.768763Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:00:31.768763Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MaskNet: Introducing Feature-Wise Multiplication to CTR Ranking Models by Instance-Guided Mask","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Junlin Zhang, Qingyun She, Zhiqiang Wang","submitted_at":"2021-02-09T12:27:49Z","abstract_excerpt":"Click-Through Rate(CTR) estimation has become one of the most fundamental tasks in many real-world applications and it's important for ranking models to effectively capture complex high-order features. Shallow feed-forward network is widely used in many state-of-the-art DNN models such as FNN, DeepFM and xDeepFM to implicitly capture high-order feature interactions. However, some research has proved that addictive feature interaction, particular feed-forward neural networks, is inefficient in capturing common feature interaction. To resolve this problem, we introduce specific multiplicative op"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2102.07619","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2102.07619/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2102.07619","created_at":"2026-07-05T03:00:31.768820+00:00"},{"alias_kind":"arxiv_version","alias_value":"2102.07619v2","created_at":"2026-07-05T03:00:31.768820+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2102.07619","created_at":"2026-07-05T03:00:31.768820+00:00"},{"alias_kind":"pith_short_12","alias_value":"Q57WZY2EBP74","created_at":"2026-07-05T03:00:31.768820+00:00"},{"alias_kind":"pith_short_16","alias_value":"Q57WZY2EBP747WBK","created_at":"2026-07-05T03:00:31.768820+00:00"},{"alias_kind":"pith_short_8","alias_value":"Q57WZY2E","created_at":"2026-07-05T03:00:31.768820+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":5,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.04944","citing_title":"Dual-Stream MLP is All You Need for CTR Prediction","ref_index":53,"is_internal_anchor":false},{"citing_arxiv_id":"2605.29232","citing_title":"On the Practice of Scaling Search Conversion Rate Prediction","ref_index":28,"is_internal_anchor":false},{"citing_arxiv_id":"2604.20847","citing_title":"Revisiting Content-Based Music Recommendation: Efficient Feature Aggregation from Large-Scale Music Models","ref_index":41,"is_internal_anchor":false},{"citing_arxiv_id":"2604.12234","citing_title":"UniRec: Bridging the Expressive Gap between Generative and Discriminative Recommendation via Chain-of-Attribute","ref_index":18,"is_internal_anchor":false},{"citing_arxiv_id":"2604.08011","citing_title":"Beyond Dense Connectivity: Explicit Sparsity for Scalable Recommendation","ref_index":33,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/Q57WZY2EBP747WBKHUHPWWCC25","json":"https://pith.science/pith/Q57WZY2EBP747WBKHUHPWWCC25.json","graph_json":"https://pith.science/api/pith-number/Q57WZY2EBP747WBKHUHPWWCC25/graph.json","events_json":"https://pith.science/api/pith-number/Q57WZY2EBP747WBKHUHPWWCC25/events.json","paper":"https://pith.science/paper/Q57WZY2E"},"agent_actions":{"view_html":"https://pith.science/pith/Q57WZY2EBP747WBKHUHPWWCC25","download_json":"https://pith.science/pith/Q57WZY2EBP747WBKHUHPWWCC25.json","view_paper":"https://pith.science/paper/Q57WZY2E","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2102.07619&json=true","fetch_graph":"https://pith.science/api/pith-number/Q57WZY2EBP747WBKHUHPWWCC25/graph.json","fetch_events":"https://pith.science/api/pith-number/Q57WZY2EBP747WBKHUHPWWCC25/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Q57WZY2EBP747WBKHUHPWWCC25/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Q57WZY2EBP747WBKHUHPWWCC25/action/storage_attestation","attest_author":"https://pith.science/pith/Q57WZY2EBP747WBKHUHPWWCC25/action/author_attestation","sign_citation":"https://pith.science/pith/Q57WZY2EBP747WBKHUHPWWCC25/action/citation_signature","submit_replication":"https://pith.science/pith/Q57WZY2EBP747WBKHUHPWWCC25/action/replication_record"}},"created_at":"2026-07-05T03:00:31.768820+00:00","updated_at":"2026-07-05T03:00:31.768820+00:00"}