{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003821879,0.001606952,0.001721597,0.0009362409,0.0008783508,0.001632959,0.002159782,0.002056156,0.003336537],"category_scores_gemma":[0.01807112,0.001195528,0.001276637,0.0009860054,0.001394183,0.003158794,0.002194698,0.002941626,0.001627313],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001581499,"about_ca_system_score_gemma":0.001413869,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0072784,"about_ca_topic_score_gemma":0.00999334,"domain_scores_codex":[0.9957892,0.002285568,0.0002355802,0.0008120587,0.0007223998,0.0001550601],"domain_scores_gemma":[0.9905069,0.006850075,0.0005444718,0.0006647645,0.001228211,0.0002055165],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004446931,0.0002834353,0.002406873,0.0007763761,0.000286774,0.0002127412,0.001417122,0.6464554,0.0112957,0.0306307,0.005701421,0.3000888],"study_design_scores_gemma":[0.00001719949,0.00004742142,0.00009626939,0.00001381284,0.00001769393,0.00002090734,0.00003711686,0.9869384,0.0008292687,0.01090198,0.001060748,0.00001915816],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00513247,0.0005375805,0.9927205,0.0002147509,0.00002909419,0.00006450876,0.00005633889,0.0005355562,0.0007092646],"genre_scores_gemma":[0.4676755,0.0008529177,0.5247126,0.0004941825,0.0002556058,0.0006100599,0.0005636201,0.000307858,0.004527536],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0072784,"threshold_uncertainty_score":0.02021229,"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."}}