{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:ZYUQDC6PDQSNC5U5EHOEZUJAWB","short_pith_number":"pith:ZYUQDC6P","schema_version":"1.0","canonical_sha256":"ce29018bcf1c24d1769d21dc4cd120b04da5b1e823bc583cddf811747188c8e8","source":{"kind":"arxiv","id":"1906.10304","version":1},"attestation_state":"computed","paper":{"title":"Res-embedding for Deep Learning Based Click-Through Rate Prediction Modeling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Guorui Zhou, Kailun Wu, Kun Gai, Weijie Bian, Xiaoqiang Zhu, Zhao Yang","submitted_at":"2019-06-25T03:13:43Z","abstract_excerpt":"Recently, click-through rate (CTR) prediction models have evolved from shallow methods to deep neural networks. Most deep CTR models follow an Embedding\\&MLP paradigm, that is, first mapping discrete id features, e.g. user visited items, into low dimensional vectors with an embedding module, then learn a multi-layer perception (MLP) to fit the target. In this way, embedding module performs as the representative learning and plays a key role in the model performance. However, in many real-world applications, deep CTR model often suffers from poor generalization performance, which is mostly due "},"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":"1906.10304","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-06-25T03:13:43Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"2da7aa55c1387e845b6a9c7add31ce1604c99327283171cdff9e9cb58c5d8658","abstract_canon_sha256":"d1874e63b5429c43563236a078526d8893f4ee593a1d017a31b1373c84160d09"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:42:31.674743Z","signature_b64":"S9dryzAcg9OPaFjcNkbOP6Zj1fZDb2L6Ydxr/9CknHWUbo/23euDG9uvWQdrBWRgQJPKzFVLPebrggASXZmkDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ce29018bcf1c24d1769d21dc4cd120b04da5b1e823bc583cddf811747188c8e8","last_reissued_at":"2026-05-17T23:42:31.674163Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:42:31.674163Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Res-embedding for Deep Learning Based Click-Through Rate Prediction Modeling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Guorui Zhou, Kailun Wu, Kun Gai, Weijie Bian, Xiaoqiang Zhu, Zhao Yang","submitted_at":"2019-06-25T03:13:43Z","abstract_excerpt":"Recently, click-through rate (CTR) prediction models have evolved from shallow methods to deep neural networks. Most deep CTR models follow an Embedding\\&MLP paradigm, that is, first mapping discrete id features, e.g. user visited items, into low dimensional vectors with an embedding module, then learn a multi-layer perception (MLP) to fit the target. In this way, embedding module performs as the representative learning and plays a key role in the model performance. However, in many real-world applications, deep CTR model often suffers from poor generalization performance, which is mostly due "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.10304","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":"1906.10304","created_at":"2026-05-17T23:42:31.674246+00:00"},{"alias_kind":"arxiv_version","alias_value":"1906.10304v1","created_at":"2026-05-17T23:42:31.674246+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.10304","created_at":"2026-05-17T23:42:31.674246+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZYUQDC6PDQSN","created_at":"2026-05-18T12:33:33.725879+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZYUQDC6PDQSNC5U5","created_at":"2026-05-18T12:33:33.725879+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZYUQDC6P","created_at":"2026-05-18T12:33:33.725879+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/ZYUQDC6PDQSNC5U5EHOEZUJAWB","json":"https://pith.science/pith/ZYUQDC6PDQSNC5U5EHOEZUJAWB.json","graph_json":"https://pith.science/api/pith-number/ZYUQDC6PDQSNC5U5EHOEZUJAWB/graph.json","events_json":"https://pith.science/api/pith-number/ZYUQDC6PDQSNC5U5EHOEZUJAWB/events.json","paper":"https://pith.science/paper/ZYUQDC6P"},"agent_actions":{"view_html":"https://pith.science/pith/ZYUQDC6PDQSNC5U5EHOEZUJAWB","download_json":"https://pith.science/pith/ZYUQDC6PDQSNC5U5EHOEZUJAWB.json","view_paper":"https://pith.science/paper/ZYUQDC6P","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1906.10304&json=true","fetch_graph":"https://pith.science/api/pith-number/ZYUQDC6PDQSNC5U5EHOEZUJAWB/graph.json","fetch_events":"https://pith.science/api/pith-number/ZYUQDC6PDQSNC5U5EHOEZUJAWB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZYUQDC6PDQSNC5U5EHOEZUJAWB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZYUQDC6PDQSNC5U5EHOEZUJAWB/action/storage_attestation","attest_author":"https://pith.science/pith/ZYUQDC6PDQSNC5U5EHOEZUJAWB/action/author_attestation","sign_citation":"https://pith.science/pith/ZYUQDC6PDQSNC5U5EHOEZUJAWB/action/citation_signature","submit_replication":"https://pith.science/pith/ZYUQDC6PDQSNC5U5EHOEZUJAWB/action/replication_record"}},"created_at":"2026-05-17T23:42:31.674246+00:00","updated_at":"2026-05-17T23:42:31.674246+00:00"}