{"id":"W3154508774","doi":"10.1145/3404835.3462986","title":"Variational Autoencoders for Top-K Recommendation with Implicit Feedback","year":2021,"lang":"en","type":"article","venue":"","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Computer science; Pairwise comparison; Autoencoder; Hinge; Recommender system; Hinge loss; Variety (cybernetics); Set (abstract data type); Artificial intelligence; Machine learning; Preference; Data mining; Information retrieval; Deep learning; Mathematics","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":[0.0001876629,0.00008172697,0.0001044996,0.00004050158,0.00009574339,0.0001804666,0.0002046104,0.00003946203,0.00009950314],"category_scores_gemma":[0.00001170905,0.0000648097,0.00004001983,0.0001916892,0.000005335241,0.0003835362,0.00006733309,0.00004134922,0.000007485405],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000386439,"about_ca_system_score_gemma":0.0001068149,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004317983,"about_ca_topic_score_gemma":0.00005315572,"domain_scores_codex":[0.999273,0.0000293042,0.0001617724,0.0002847295,0.0000983775,0.0001528133],"domain_scores_gemma":[0.9993792,0.00008751848,0.00006164049,0.0002555067,0.0001726496,0.00004346195],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000006002914,0.00007643183,0.0009020146,0.00002649981,0.00005052022,0.00000233455,0.0002316656,0.00006908536,0.0002940262,0.8812634,0.05286518,0.06421288],"study_design_scores_gemma":[0.001611757,0.0004153078,0.005760708,0.00005674753,0.00001621606,0.0001992708,0.0002190641,0.4214011,0.02130953,0.07602984,0.4723024,0.0006780752],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00009149133,0.000007077664,0.9657797,0.01235462,0.0001857809,0.0001769332,0.000003584962,0.0002301421,0.02117069],"genre_scores_gemma":[0.05905615,0.000004577954,0.9376407,0.00145781,0.00007396597,0.0001087977,0.00003819845,0.000008380965,0.001611461],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8052335,"threshold_uncertainty_score":0.2642863,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01943975929359573,"score_gpt":0.261621550567817,"score_spread":0.2421817912742213,"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."}}