{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:DAPGWDHHXCOPPEZOHXKF5GMQLA","short_pith_number":"pith:DAPGWDHH","schema_version":"1.0","canonical_sha256":"181e6b0ce7b89cf7932e3dd45e999058381bc90124f934fb0c230f6048935923","source":{"kind":"arxiv","id":"1807.06161","version":1},"attestation_state":"computed","paper":{"title":"Explanations for Temporal Recommendations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.HC","cs.IR","cs.LG"],"primary_cat":"cs.AI","authors_text":"Homanga Bharadhwaj, Shruti Joshi","submitted_at":"2018-07-17T00:38:40Z","abstract_excerpt":"Recommendation systems are an integral part of Artificial Intelligence (AI) and have become increasingly important in the growing age of commercialization in AI. Deep learning (DL) techniques for recommendation systems (RS) provide powerful latent-feature models for effective recommendation but suffer from the major drawback of being non-interpretable. In this paper we describe a framework for explainable temporal recommendations in a DL model. We consider an LSTM based Recurrent Neural Network (RNN) architecture for recommendation and a neighbourhood-based scheme for generating explanations i"},"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":"1807.06161","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2018-07-17T00:38:40Z","cross_cats_sorted":["cs.HC","cs.IR","cs.LG"],"title_canon_sha256":"a69961a5d7a62eb27dfe783fd1669c61b32dda66c708d0af7922e24ffbe6a0cb","abstract_canon_sha256":"72b07fd229329c5b4e84fd81d09c012a17776b49023e69c0a9088e02154fc265"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:10:33.829777Z","signature_b64":"TiSNd+eoQ+gO55aUhzzWXzBhfnwHi/95AVksp13vh0Dwple6RL3esnWg8fnBR/IXbo6dIqzaOZ8l2GzK/q/YCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"181e6b0ce7b89cf7932e3dd45e999058381bc90124f934fb0c230f6048935923","last_reissued_at":"2026-05-18T00:10:33.829172Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:10:33.829172Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Explanations for Temporal Recommendations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.HC","cs.IR","cs.LG"],"primary_cat":"cs.AI","authors_text":"Homanga Bharadhwaj, Shruti Joshi","submitted_at":"2018-07-17T00:38:40Z","abstract_excerpt":"Recommendation systems are an integral part of Artificial Intelligence (AI) and have become increasingly important in the growing age of commercialization in AI. Deep learning (DL) techniques for recommendation systems (RS) provide powerful latent-feature models for effective recommendation but suffer from the major drawback of being non-interpretable. In this paper we describe a framework for explainable temporal recommendations in a DL model. We consider an LSTM based Recurrent Neural Network (RNN) architecture for recommendation and a neighbourhood-based scheme for generating explanations i"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.06161","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":"1807.06161","created_at":"2026-05-18T00:10:33.829256+00:00"},{"alias_kind":"arxiv_version","alias_value":"1807.06161v1","created_at":"2026-05-18T00:10:33.829256+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.06161","created_at":"2026-05-18T00:10:33.829256+00:00"},{"alias_kind":"pith_short_12","alias_value":"DAPGWDHHXCOP","created_at":"2026-05-18T12:32:19.392346+00:00"},{"alias_kind":"pith_short_16","alias_value":"DAPGWDHHXCOPPEZO","created_at":"2026-05-18T12:32:19.392346+00:00"},{"alias_kind":"pith_short_8","alias_value":"DAPGWDHH","created_at":"2026-05-18T12:32:19.392346+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/DAPGWDHHXCOPPEZOHXKF5GMQLA","json":"https://pith.science/pith/DAPGWDHHXCOPPEZOHXKF5GMQLA.json","graph_json":"https://pith.science/api/pith-number/DAPGWDHHXCOPPEZOHXKF5GMQLA/graph.json","events_json":"https://pith.science/api/pith-number/DAPGWDHHXCOPPEZOHXKF5GMQLA/events.json","paper":"https://pith.science/paper/DAPGWDHH"},"agent_actions":{"view_html":"https://pith.science/pith/DAPGWDHHXCOPPEZOHXKF5GMQLA","download_json":"https://pith.science/pith/DAPGWDHHXCOPPEZOHXKF5GMQLA.json","view_paper":"https://pith.science/paper/DAPGWDHH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1807.06161&json=true","fetch_graph":"https://pith.science/api/pith-number/DAPGWDHHXCOPPEZOHXKF5GMQLA/graph.json","fetch_events":"https://pith.science/api/pith-number/DAPGWDHHXCOPPEZOHXKF5GMQLA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DAPGWDHHXCOPPEZOHXKF5GMQLA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DAPGWDHHXCOPPEZOHXKF5GMQLA/action/storage_attestation","attest_author":"https://pith.science/pith/DAPGWDHHXCOPPEZOHXKF5GMQLA/action/author_attestation","sign_citation":"https://pith.science/pith/DAPGWDHHXCOPPEZOHXKF5GMQLA/action/citation_signature","submit_replication":"https://pith.science/pith/DAPGWDHHXCOPPEZOHXKF5GMQLA/action/replication_record"}},"created_at":"2026-05-18T00:10:33.829256+00:00","updated_at":"2026-05-18T00:10:33.829256+00:00"}