{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:K6OW6BNWRRUGKSU33FR2OJNE6R","short_pith_number":"pith:K6OW6BNW","schema_version":"1.0","canonical_sha256":"579d6f05b68c68654a9bd963a725a4f467e751e591f41140461ba897cd94563a","source":{"kind":"arxiv","id":"1810.02019","version":3},"attestation_state":"computed","paper":{"title":"Seq2Slate: Re-ranking and Slate Optimization with RNNs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.IR","authors_text":"Alan Mackey, Craig Boutilier, Ed Chi, Elad Eban, Irwan Bello, Ofer Meshi, Sagar Jain, Sayali Kulkarni, Xiyang Luo","submitted_at":"2018-10-04T01:35:14Z","abstract_excerpt":"Ranking is a central task in machine learning and information retrieval. In this task, it is especially important to present the user with a slate of items that is appealing as a whole. This in turn requires taking into account interactions between items, since intuitively, placing an item on the slate affects the decision of which other items should be placed alongside it. In this work, we propose a sequence-to-sequence model for ranking called seq2slate. At each step, the model predicts the next `best' item to place on the slate given the items already selected. The sequential nature of the "},"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":"1810.02019","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2018-10-04T01:35:14Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"a232f0ff3d3a383f0f0004fc052618466a858e930c7d8fdf6f4affc436b9cbc8","abstract_canon_sha256":"1e9885ff70118dc16194640fc3eecd50ad081816e1908b63254cb05615059a32"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:50:50.495816Z","signature_b64":"JSzdnZym7f3he8DaS0C8DEY3gE6wCL/Xk7li+Bb5t2fUj0WQc46lPpKeBOq5F3JAsCS4wKCBXZxHwShIsL6kDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"579d6f05b68c68654a9bd963a725a4f467e751e591f41140461ba897cd94563a","last_reissued_at":"2026-05-17T23:50:50.495093Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:50:50.495093Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Seq2Slate: Re-ranking and Slate Optimization with RNNs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.IR","authors_text":"Alan Mackey, Craig Boutilier, Ed Chi, Elad Eban, Irwan Bello, Ofer Meshi, Sagar Jain, Sayali Kulkarni, Xiyang Luo","submitted_at":"2018-10-04T01:35:14Z","abstract_excerpt":"Ranking is a central task in machine learning and information retrieval. In this task, it is especially important to present the user with a slate of items that is appealing as a whole. This in turn requires taking into account interactions between items, since intuitively, placing an item on the slate affects the decision of which other items should be placed alongside it. In this work, we propose a sequence-to-sequence model for ranking called seq2slate. At each step, the model predicts the next `best' item to place on the slate given the items already selected. The sequential nature of the "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.02019","kind":"arxiv","version":3},"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":"1810.02019","created_at":"2026-05-17T23:50:50.495214+00:00"},{"alias_kind":"arxiv_version","alias_value":"1810.02019v3","created_at":"2026-05-17T23:50:50.495214+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.02019","created_at":"2026-05-17T23:50:50.495214+00:00"},{"alias_kind":"pith_short_12","alias_value":"K6OW6BNWRRUG","created_at":"2026-05-18T12:32:33.847187+00:00"},{"alias_kind":"pith_short_16","alias_value":"K6OW6BNWRRUGKSU3","created_at":"2026-05-18T12:32:33.847187+00:00"},{"alias_kind":"pith_short_8","alias_value":"K6OW6BNW","created_at":"2026-05-18T12:32:33.847187+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":6,"internal_anchor_count":2,"sample":[{"citing_arxiv_id":"2602.03324","citing_title":"SCASRec: A Self-Correcting and Auto-Stopping Model for Generative Route List Recommendation","ref_index":4,"is_internal_anchor":true},{"citing_arxiv_id":"2602.05408","citing_title":"Rich-Media Re-Ranker: A User Satisfaction-Driven LLM Re-ranking Framework for Rich-Media Search","ref_index":4,"is_internal_anchor":true},{"citing_arxiv_id":"2605.10527","citing_title":"UniRank: Unified List-wise Reranking via Confidence-Ordered Denoising","ref_index":6,"is_internal_anchor":false},{"citing_arxiv_id":"2604.25291","citing_title":"From Local Indices to Global Identifiers: Generative Reranking for Recommender Systems via Global Action Space","ref_index":3,"is_internal_anchor":false},{"citing_arxiv_id":"2604.07420","citing_title":"Dual-Rerank: Fusing Causality and Utility for Industrial Generative Reranking","ref_index":2,"is_internal_anchor":false},{"citing_arxiv_id":"2604.05314","citing_title":"Next-Scale Generative Reranking: A Tree-based Generative Rerank Method at Meituan","ref_index":4,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/K6OW6BNWRRUGKSU33FR2OJNE6R","json":"https://pith.science/pith/K6OW6BNWRRUGKSU33FR2OJNE6R.json","graph_json":"https://pith.science/api/pith-number/K6OW6BNWRRUGKSU33FR2OJNE6R/graph.json","events_json":"https://pith.science/api/pith-number/K6OW6BNWRRUGKSU33FR2OJNE6R/events.json","paper":"https://pith.science/paper/K6OW6BNW"},"agent_actions":{"view_html":"https://pith.science/pith/K6OW6BNWRRUGKSU33FR2OJNE6R","download_json":"https://pith.science/pith/K6OW6BNWRRUGKSU33FR2OJNE6R.json","view_paper":"https://pith.science/paper/K6OW6BNW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1810.02019&json=true","fetch_graph":"https://pith.science/api/pith-number/K6OW6BNWRRUGKSU33FR2OJNE6R/graph.json","fetch_events":"https://pith.science/api/pith-number/K6OW6BNWRRUGKSU33FR2OJNE6R/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/K6OW6BNWRRUGKSU33FR2OJNE6R/action/timestamp_anchor","attest_storage":"https://pith.science/pith/K6OW6BNWRRUGKSU33FR2OJNE6R/action/storage_attestation","attest_author":"https://pith.science/pith/K6OW6BNWRRUGKSU33FR2OJNE6R/action/author_attestation","sign_citation":"https://pith.science/pith/K6OW6BNWRRUGKSU33FR2OJNE6R/action/citation_signature","submit_replication":"https://pith.science/pith/K6OW6BNWRRUGKSU33FR2OJNE6R/action/replication_record"}},"created_at":"2026-05-17T23:50:50.495214+00:00","updated_at":"2026-05-17T23:50:50.495214+00:00"}