{"id":"W3034522014","doi":"10.1145/3397271.3401091","title":"Deep Critiquing for VAE-based Recommender Systems","year":2020,"lang":"en","type":"article","venue":"","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Recommender system; Popularity; Autoencoder; Constraint (computer-aided design); Artificial intelligence; Deep learning; Aside; Rank (graph theory); Quality (philosophy); Function (biology); Information retrieval; Machine learning; Data science","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":[0.0002912559,0.0001352448,0.0002126813,0.00004977105,0.0001023618,0.0003123142,0.0006537875,0.00006605605,0.00001383167],"category_scores_gemma":[0.00003474869,0.0001153878,0.00009900744,0.0001798512,0.000007849758,0.0003099335,0.00009734018,0.00007486121,0.00001578026],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002960566,"about_ca_system_score_gemma":0.00003246965,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005304547,"about_ca_topic_score_gemma":0.000004205287,"domain_scores_codex":[0.998863,0.000065075,0.0002921472,0.0003761751,0.0001386967,0.0002648834],"domain_scores_gemma":[0.9991838,0.0001639877,0.00007117355,0.0003400631,0.00009450288,0.0001464738],"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":[0.00001783796,0.0001064919,0.0004314097,0.0006570883,0.00006830082,0.00001088284,0.001112289,0.0004318545,0.0009167499,0.6082804,0.3316483,0.05631838],"study_design_scores_gemma":[0.0003449169,0.0001977921,0.00001009904,0.00002923701,0.000003547432,0.000003737692,0.00009686082,0.8289742,0.003516481,0.0009689253,0.1656313,0.0002228596],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00002392339,0.0001432986,0.9727448,0.01789068,0.0004558942,0.0004989781,0.000001498073,0.0009048402,0.007336074],"genre_scores_gemma":[0.7200133,0.000004115401,0.271551,0.007817376,0.0002213017,0.0002459678,0.000003784314,0.00001965453,0.0001235538],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8285424,"threshold_uncertainty_score":0.4705378,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06524802760879765,"score_gpt":0.2782721761367052,"score_spread":0.2130241485279076,"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."}}