{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:QD2OPJYYMA2FWLRMKTFFQ3PYF3","short_pith_number":"pith:QD2OPJYY","schema_version":"1.0","canonical_sha256":"80f4e7a71860345b2e2c54ca586df82edb3e310dc9d65c7f0d05310e78b9b94d","source":{"kind":"arxiv","id":"2102.09388","version":3},"attestation_state":"computed","paper":{"title":"ELIXIR: Learning from User Feedback on Explanations to Improve Recommender Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.IR","authors_text":"Azin Ghazimatin, Gerhard Weikum, Rishiraj Saha Roy, Soumajit Pramanik","submitted_at":"2021-02-15T13:43:49Z","abstract_excerpt":"System-provided explanations for recommendations are an important component towards transparent and trustworthy AI. In state-of-the-art research, this is a one-way signal, though, to improve user acceptance. In this paper, we turn the role of explanations around and investigate how they can contribute to enhancing the quality of the generated recommendations themselves. We devise a human-in-the-loop framework, called ELIXIR, where user feedback on explanations is leveraged for pairwise learning of user preferences. ELIXIR leverages feedback on pairs of recommendations and explanations to learn"},"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":"2102.09388","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2021-02-15T13:43:49Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"78dab15b32a47f779d63bb8d6ab72bdb07cd6808bd355b3c2b11bb34afdd1ebb","abstract_canon_sha256":"5ad21180d13228cfac3273bf3d364ddc6620dd021a5c6f1248cac1cd60d6cc1f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:36:32.385584Z","signature_b64":"41JTZCZeque3bn0MQ90LxJyznnCf0j0FHWknVyDH2808cIRgdOKk0pSg+cITXk+9NXcu6fPYUdYFFWW6bxzaAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"80f4e7a71860345b2e2c54ca586df82edb3e310dc9d65c7f0d05310e78b9b94d","last_reissued_at":"2026-07-05T02:36:32.385129Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:36:32.385129Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ELIXIR: Learning from User Feedback on Explanations to Improve Recommender Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.IR","authors_text":"Azin Ghazimatin, Gerhard Weikum, Rishiraj Saha Roy, Soumajit Pramanik","submitted_at":"2021-02-15T13:43:49Z","abstract_excerpt":"System-provided explanations for recommendations are an important component towards transparent and trustworthy AI. In state-of-the-art research, this is a one-way signal, though, to improve user acceptance. In this paper, we turn the role of explanations around and investigate how they can contribute to enhancing the quality of the generated recommendations themselves. We devise a human-in-the-loop framework, called ELIXIR, where user feedback on explanations is leveraged for pairwise learning of user preferences. ELIXIR leverages feedback on pairs of recommendations and explanations to learn"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2102.09388","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2102.09388/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":"2102.09388","created_at":"2026-07-05T02:36:32.385192+00:00"},{"alias_kind":"arxiv_version","alias_value":"2102.09388v3","created_at":"2026-07-05T02:36:32.385192+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2102.09388","created_at":"2026-07-05T02:36:32.385192+00:00"},{"alias_kind":"pith_short_12","alias_value":"QD2OPJYYMA2F","created_at":"2026-07-05T02:36:32.385192+00:00"},{"alias_kind":"pith_short_16","alias_value":"QD2OPJYYMA2FWLRM","created_at":"2026-07-05T02:36:32.385192+00:00"},{"alias_kind":"pith_short_8","alias_value":"QD2OPJYY","created_at":"2026-07-05T02:36:32.385192+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/QD2OPJYYMA2FWLRMKTFFQ3PYF3","json":"https://pith.science/pith/QD2OPJYYMA2FWLRMKTFFQ3PYF3.json","graph_json":"https://pith.science/api/pith-number/QD2OPJYYMA2FWLRMKTFFQ3PYF3/graph.json","events_json":"https://pith.science/api/pith-number/QD2OPJYYMA2FWLRMKTFFQ3PYF3/events.json","paper":"https://pith.science/paper/QD2OPJYY"},"agent_actions":{"view_html":"https://pith.science/pith/QD2OPJYYMA2FWLRMKTFFQ3PYF3","download_json":"https://pith.science/pith/QD2OPJYYMA2FWLRMKTFFQ3PYF3.json","view_paper":"https://pith.science/paper/QD2OPJYY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2102.09388&json=true","fetch_graph":"https://pith.science/api/pith-number/QD2OPJYYMA2FWLRMKTFFQ3PYF3/graph.json","fetch_events":"https://pith.science/api/pith-number/QD2OPJYYMA2FWLRMKTFFQ3PYF3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QD2OPJYYMA2FWLRMKTFFQ3PYF3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QD2OPJYYMA2FWLRMKTFFQ3PYF3/action/storage_attestation","attest_author":"https://pith.science/pith/QD2OPJYYMA2FWLRMKTFFQ3PYF3/action/author_attestation","sign_citation":"https://pith.science/pith/QD2OPJYYMA2FWLRMKTFFQ3PYF3/action/citation_signature","submit_replication":"https://pith.science/pith/QD2OPJYYMA2FWLRMKTFFQ3PYF3/action/replication_record"}},"created_at":"2026-07-05T02:36:32.385192+00:00","updated_at":"2026-07-05T02:36:32.385192+00:00"}