{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003249504,0.0001610079,0.0002139023,0.00010192,0.000156282,0.0004045078,0.0002923814,0.00007598461,0.00001531783],"category_scores_gemma":[0.00002984667,0.0001288994,0.00006787847,0.0003184672,0.00001004453,0.0005091742,0.00005117014,0.00008494292,0.000001901649],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008787005,"about_ca_system_score_gemma":0.0001453222,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001531017,"about_ca_topic_score_gemma":0.00003732981,"domain_scores_codex":[0.9987317,0.000110184,0.0002608661,0.0004344166,0.0001906991,0.0002720775],"domain_scores_gemma":[0.9987952,0.0002325872,0.0000997278,0.0005406611,0.0002421256,0.00008973714],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001292023,0.001135191,0.006881995,0.001939434,0.000440299,0.0001155111,0.001822975,0.001357891,0.01840307,0.7039241,0.1696981,0.09415223],"study_design_scores_gemma":[0.002358721,0.0007830148,0.000429533,0.0008650802,0.00002904604,0.00009140662,0.0009410857,0.4247067,0.4503638,0.003159097,0.1150109,0.001261622],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.000729479,0.00003734845,0.9901984,0.003703531,0.0003522119,0.000422094,0.000002353481,0.0004959764,0.004058614],"genre_scores_gemma":[0.8390842,0.000002104762,0.1593412,0.0007973268,0.00008610199,0.0002285245,0.00001370287,0.00001941557,0.0004273539],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8383548,"threshold_uncertainty_score":0.5256364,"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."}}