{"id":"W2955894817","doi":"10.1145/3331184.3331292","title":"One-Class Collaborative Filtering with the Queryable Variational Autoencoder","year":2019,"lang":"en","type":"article","venue":"","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Autoencoder; Computer science; Ambiguity; Collaborative filtering; Representation (politics); Preference; Benchmark (surveying); Class (philosophy); Artificial intelligence; Machine learning; Feature learning; Deep learning; Theoretical computer science; Recommender system; Mathematics; Statistics","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":[0.003822041,0.001232456,0.002341248,0.000678675,0.0006094342,0.001098552,0.002766886,0.002160904,0.001773662],"category_scores_gemma":[0.00901448,0.0008996462,0.001471162,0.001122497,0.001176441,0.002100809,0.001361538,0.002703784,0.0007339368],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00111172,"about_ca_system_score_gemma":0.001261511,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01536631,"about_ca_topic_score_gemma":0.01841637,"domain_scores_codex":[0.9980378,0.0008036948,0.0001111482,0.0005043753,0.0004012238,0.0001418882],"domain_scores_gemma":[0.9955923,0.003131868,0.0001580737,0.0005347884,0.0004878671,0.00009505807],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001620245,0.000126733,0.001362546,0.0001516772,0.0002447534,0.00009538613,0.0001820769,0.8167405,0.00287738,0.0271281,0.002978674,0.1479502],"study_design_scores_gemma":[0.000007009432,0.00001510438,0.00007012505,0.000004015417,0.000006298183,0.00001499078,0.000004238708,0.995862,0.000294908,0.003447394,0.0002670407,0.000006808154],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004515282,0.0002954846,0.9943542,0.0001178759,0.00002727761,0.00002206118,0.00004614334,0.0001679536,0.000453744],"genre_scores_gemma":[0.4975117,0.0007469385,0.4941561,0.000620303,0.0002221169,0.0002590319,0.0007008652,0.0001414794,0.005641455],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01536631,"threshold_uncertainty_score":0.03055376,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01125829192930888,"score_gpt":0.2165775548463472,"score_spread":0.2053192629170383,"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."}}