{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:RFG62EPYIM3PEE4LU6KUSEWXDC","short_pith_number":"pith:RFG62EPY","schema_version":"1.0","canonical_sha256":"894ded11f84336f2138ba7954912d7189549c157dc9c59e7543844365d33ea75","source":{"kind":"arxiv","id":"1606.05819","version":1},"attestation_state":"computed","paper":{"title":"Building an Interpretable Recommender via Loss-Preserving Transformation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Amit Dhurandhar, Marek Petrik, Sechan Oh","submitted_at":"2016-06-19T01:37:01Z","abstract_excerpt":"We propose a method for building an interpretable recommender system for personalizing online content and promotions. Historical data available for the system consists of customer features, provided content (promotions), and user responses. Unlike in a standard multi-class classification setting, misclassification costs depend on both recommended actions and customers. Our method transforms such a data set to a new set which can be used with standard interpretable multi-class classification algorithms. The transformation has the desirable property that minimizing the standard misclassification"},"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":"1606.05819","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-06-19T01:37:01Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"0e36d1e0b16acbbc9ad9a83d358502012dd156616e06b1bad4647a54c483ccd5","abstract_canon_sha256":"7cfda6345003f240f0de8c74b59c1819014fb9cac0984c514031f5bfdd585b6e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:12:15.979299Z","signature_b64":"o+Z3FkFTWnm10R4TQyrvjNRaWeUCUen58ZnSb5YhbbyT/5aeqGZJe0YSCUXVvRmVK7RZy3fOJ+zEnxdtuNpmAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"894ded11f84336f2138ba7954912d7189549c157dc9c59e7543844365d33ea75","last_reissued_at":"2026-05-18T01:12:15.978947Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:12:15.978947Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Building an Interpretable Recommender via Loss-Preserving Transformation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Amit Dhurandhar, Marek Petrik, Sechan Oh","submitted_at":"2016-06-19T01:37:01Z","abstract_excerpt":"We propose a method for building an interpretable recommender system for personalizing online content and promotions. Historical data available for the system consists of customer features, provided content (promotions), and user responses. Unlike in a standard multi-class classification setting, misclassification costs depend on both recommended actions and customers. Our method transforms such a data set to a new set which can be used with standard interpretable multi-class classification algorithms. The transformation has the desirable property that minimizing the standard misclassification"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1606.05819","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":"1606.05819","created_at":"2026-05-18T01:12:15.978999+00:00"},{"alias_kind":"arxiv_version","alias_value":"1606.05819v1","created_at":"2026-05-18T01:12:15.978999+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1606.05819","created_at":"2026-05-18T01:12:15.978999+00:00"},{"alias_kind":"pith_short_12","alias_value":"RFG62EPYIM3P","created_at":"2026-05-18T12:30:41.710351+00:00"},{"alias_kind":"pith_short_16","alias_value":"RFG62EPYIM3PEE4L","created_at":"2026-05-18T12:30:41.710351+00:00"},{"alias_kind":"pith_short_8","alias_value":"RFG62EPY","created_at":"2026-05-18T12:30:41.710351+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/RFG62EPYIM3PEE4LU6KUSEWXDC","json":"https://pith.science/pith/RFG62EPYIM3PEE4LU6KUSEWXDC.json","graph_json":"https://pith.science/api/pith-number/RFG62EPYIM3PEE4LU6KUSEWXDC/graph.json","events_json":"https://pith.science/api/pith-number/RFG62EPYIM3PEE4LU6KUSEWXDC/events.json","paper":"https://pith.science/paper/RFG62EPY"},"agent_actions":{"view_html":"https://pith.science/pith/RFG62EPYIM3PEE4LU6KUSEWXDC","download_json":"https://pith.science/pith/RFG62EPYIM3PEE4LU6KUSEWXDC.json","view_paper":"https://pith.science/paper/RFG62EPY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1606.05819&json=true","fetch_graph":"https://pith.science/api/pith-number/RFG62EPYIM3PEE4LU6KUSEWXDC/graph.json","fetch_events":"https://pith.science/api/pith-number/RFG62EPYIM3PEE4LU6KUSEWXDC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RFG62EPYIM3PEE4LU6KUSEWXDC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RFG62EPYIM3PEE4LU6KUSEWXDC/action/storage_attestation","attest_author":"https://pith.science/pith/RFG62EPYIM3PEE4LU6KUSEWXDC/action/author_attestation","sign_citation":"https://pith.science/pith/RFG62EPYIM3PEE4LU6KUSEWXDC/action/citation_signature","submit_replication":"https://pith.science/pith/RFG62EPYIM3PEE4LU6KUSEWXDC/action/replication_record"}},"created_at":"2026-05-18T01:12:15.978999+00:00","updated_at":"2026-05-18T01:12:15.978999+00:00"}