{"id":"W3153956316","doi":"10.1145/3404835.3463108","title":"Bayesian Critiquing with Keyphrase Activation Vectors for VAE-based Recommender Systems","year":2021,"lang":"en","type":"article","venue":"","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Bayesian probability; Recommender system; Artificial intelligence; Preference; Information retrieval; Encoding (memory); Machine learning","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":[0.004202138,0.001839067,0.001349179,0.001729205,0.0006983005,0.001595218,0.00226993,0.00235301,0.004049383],"category_scores_gemma":[0.02862308,0.0007947758,0.001240829,0.001411096,0.0008789779,0.003968928,0.001656684,0.003458802,0.002044896],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001313672,"about_ca_system_score_gemma":0.001332401,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00713228,"about_ca_topic_score_gemma":0.01271726,"domain_scores_codex":[0.9955729,0.002279716,0.0003092138,0.0009519143,0.0007303224,0.0001559544],"domain_scores_gemma":[0.9872282,0.009415135,0.0004835185,0.0009759959,0.001711326,0.0001858755],"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":[0.0008094379,0.0004539774,0.006440931,0.001131115,0.0004118399,0.0002216541,0.001685894,0.1616661,0.01549639,0.01902938,0.01430406,0.7783493],"study_design_scores_gemma":[0.0001067722,0.0002119604,0.001058248,0.0001004461,0.00008844472,0.0001778641,0.0001855852,0.9555663,0.006729254,0.02839488,0.007297498,0.00008270595],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02201143,0.002900809,0.9673247,0.000690442,0.0001258992,0.0003345705,0.0005802669,0.003643732,0.002388096],"genre_scores_gemma":[0.4518083,0.000990539,0.5391514,0.0007238732,0.0001538313,0.0005662672,0.001930128,0.0002984772,0.00437715],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00713228,"threshold_uncertainty_score":0.02222329,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02461535311590963,"score_gpt":0.2599433002678745,"score_spread":0.2353279471519649,"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."}}