{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003356411,0.001381163,0.002137236,0.0008332431,0.000619129,0.001047734,0.002407601,0.001833744,0.00227546],"category_scores_gemma":[0.01013688,0.001244225,0.001345575,0.001326552,0.001013281,0.002062573,0.001289991,0.003121556,0.001147525],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001355407,"about_ca_system_score_gemma":0.001677877,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01768037,"about_ca_topic_score_gemma":0.02570074,"domain_scores_codex":[0.9985291,0.0006258069,0.0001029346,0.0003227142,0.0003017808,0.0001177201],"domain_scores_gemma":[0.994688,0.003882838,0.0002228808,0.0005705443,0.0005223811,0.000113316],"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":[0.0001168239,0.0001044478,0.001310739,0.0001368684,0.000184452,0.00005794455,0.0001019632,0.8790541,0.001369694,0.01238245,0.003288902,0.1018916],"study_design_scores_gemma":[0.000004635895,0.00001004596,0.0000833024,0.000006015209,0.000006218256,0.00000700895,0.0000030691,0.995596,0.0001483856,0.003931952,0.0001987078,0.000004687451],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01405534,0.0016742,0.9816285,0.0003224224,0.00006574873,0.00004950684,0.0002276133,0.0007181455,0.001258543],"genre_scores_gemma":[0.6206778,0.001713484,0.3660066,0.0006932919,0.0002563143,0.0002965703,0.001710268,0.0003000089,0.008345722],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01768037,"threshold_uncertainty_score":0.03515494,"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."}}