{"id":"W3012807747","doi":"10.1145/3366423.3380003","title":"Latent Linear Critiquing for Conversational Recommender Systems","year":2020,"lang":"en","type":"article","venue":"","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Recommender system; Preference; Embedding; Information retrieval; Constraint (computer-aided design); Artificial intelligence; Machine learning; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002290038,0.00009246088,0.0001451691,0.00003111668,0.00007376348,0.0001388429,0.0003737259,0.00004823707,0.00001874421],"category_scores_gemma":[0.00002364799,0.00007905495,0.00006741674,0.00009978709,0.000006993972,0.0003062164,0.0001028361,0.00005820124,0.0000225335],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002477394,"about_ca_system_score_gemma":0.00002807862,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004100509,"about_ca_topic_score_gemma":8.459812e-7,"domain_scores_codex":[0.9991499,0.00003729011,0.0002416057,0.0002729844,0.0001307589,0.0001674585],"domain_scores_gemma":[0.9994565,0.00009987667,0.00005776995,0.0001766528,0.0001057893,0.0001034461],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000007146333,0.00003478784,0.0005114281,0.000163501,0.00005247948,0.000002938939,0.0009488377,0.00008109001,0.0003938481,0.733357,0.2587819,0.005665101],"study_design_scores_gemma":[0.0004091793,0.0001798373,0.00005258732,0.00002531399,0.000003868337,0.000008717872,0.0001169022,0.7062657,0.002424599,0.001422515,0.2888657,0.0002250663],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00008234812,0.00006020888,0.9713107,0.02416961,0.000471323,0.0003688819,0.000004286604,0.0004793295,0.003053345],"genre_scores_gemma":[0.803948,0.00001195541,0.1895835,0.005551659,0.000372394,0.0001379256,0.000009145861,0.00001383316,0.0003715524],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8038657,"threshold_uncertainty_score":0.3223768,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09068568785880386,"score_gpt":0.2902318589902802,"score_spread":0.1995461711314763,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}