{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001308175,0.001018779,0.0007047817,0.0006055408,0.0003627571,0.0006908795,0.001576412,0.00164529,0.002441484],"category_scores_gemma":[0.006537862,0.0006034191,0.0007358347,0.000360214,0.0006650355,0.001419582,0.001198869,0.002027778,0.0006235999],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008622548,"about_ca_system_score_gemma":0.0009685161,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007036204,"about_ca_topic_score_gemma":0.01377582,"domain_scores_codex":[0.99924,0.0002798202,0.00006398636,0.0002103798,0.0001502333,0.00005554324],"domain_scores_gemma":[0.9970951,0.001862442,0.0001816019,0.0002498319,0.0005234857,0.00008754658],"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":[0.0004995696,0.0003287538,0.005293964,0.0006175089,0.0002921059,0.0003334586,0.00147167,0.3636934,0.03545254,0.01120402,0.00650038,0.5743126],"study_design_scores_gemma":[0.00001984433,0.00007438981,0.0004321675,0.00002254154,0.00002743079,0.00004801232,0.00004069557,0.989484,0.004340906,0.004019267,0.001472102,0.00001861122],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0309422,0.0009616304,0.9629102,0.0003885138,0.00006563427,0.0001383947,0.0001590274,0.002814586,0.001619773],"genre_scores_gemma":[0.6497727,0.0004580762,0.3435801,0.0003964565,0.00006973808,0.0002398951,0.0004806974,0.0001412485,0.00486108],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007036204,"threshold_uncertainty_score":0.01399052,"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."}}