{"id":"W3173206275","doi":"10.1145/3450613.3456814","title":"Bayesian Preference Elicitation with Keyphrase-Item Coembeddings for Interactive Recommendation","year":2021,"lang":"en","type":"article","venue":"","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Preference elicitation; Preference; Bayesian probability; Artificial intelligence; Information retrieval; Natural language processing; Machine learning; Statistics; 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":[0.007815049,0.001483324,0.001861439,0.001452911,0.0008499735,0.001911086,0.002463691,0.001903389,0.004803779],"category_scores_gemma":[0.05369413,0.001266431,0.001734849,0.0019323,0.001493922,0.003871149,0.003550513,0.003890855,0.001575442],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001778485,"about_ca_system_score_gemma":0.00215986,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00655811,"about_ca_topic_score_gemma":0.01232046,"domain_scores_codex":[0.9911606,0.004566683,0.0004887478,0.001521413,0.001968164,0.0002945482],"domain_scores_gemma":[0.9659788,0.026033,0.001500321,0.003516177,0.002322285,0.0006494145],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001471655,0.0005559563,0.003999547,0.0007060434,0.000356728,0.0004094965,0.002265563,0.2810686,0.0178346,0.1082189,0.006410198,0.5767026],"study_design_scores_gemma":[0.00007834796,0.0001258191,0.0005622907,0.00004483614,0.00003710494,0.0001037258,0.00008937035,0.9282117,0.004200489,0.06387001,0.002610105,0.00006611612],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003912934,0.00009998937,0.9949344,0.00008182372,0.0000109751,0.0000876752,0.00009475953,0.0003036345,0.0004739078],"genre_scores_gemma":[0.269839,0.0002768768,0.7246822,0.000308985,0.00009399025,0.0007201774,0.0009180838,0.0001957097,0.002964953],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007815049,"threshold_uncertainty_score":0.0413304,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03441191552746346,"score_gpt":0.2826170399557813,"score_spread":0.2482051244283179,"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."}}