{"id":"W2405494619","doi":"10.1145/2983533","title":"Structural Analysis of User Choices for Mobile App Recommendation","year":2016,"lang":"en","type":"article","venue":"ACM Transactions on Knowledge Discovery from Data","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Computer science; Mobile apps; App store; Variety (cybernetics); Recommender system; World Wide Web; Margin (machine learning); Focus (optics); Taxonomy (biology); Data science; Human–computer interaction; Artificial intelligence; Machine learning","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":[0.004507178,0.0008160123,0.001312896,0.002259586,0.000801587,0.000975322,0.00177839,0.001598286,0.005125963],"category_scores_gemma":[0.02138644,0.0008551861,0.002093413,0.002427602,0.001008918,0.002695148,0.001039954,0.001993058,0.0009657572],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00199139,"about_ca_system_score_gemma":0.001346326,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01650641,"about_ca_topic_score_gemma":0.03453283,"domain_scores_codex":[0.9972163,0.001549976,0.0001303551,0.0005795545,0.0003355131,0.0001882523],"domain_scores_gemma":[0.9807716,0.01585925,0.001023721,0.001203682,0.0007573565,0.0003843436],"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":[0.001035654,0.0009398473,0.1917157,0.0007723205,0.001053837,0.0006155616,0.001822358,0.4648748,0.003829166,0.1345475,0.009059825,0.1897334],"study_design_scores_gemma":[0.00001557496,0.00008021106,0.007134695,0.0000231939,0.00003452053,0.00006717318,0.00007924354,0.9630389,0.0001982862,0.02845795,0.0008494901,0.00002075215],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3808426,0.001144491,0.6096746,0.001986672,0.00006141481,0.0004205467,0.002328772,0.0004809297,0.003059961],"genre_scores_gemma":[0.9377237,0.0004162717,0.05695714,0.0001588191,0.00005129493,0.0002728906,0.001768512,0.00003891182,0.002612406],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01650641,"threshold_uncertainty_score":0.03282064,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05976271325554005,"score_gpt":0.3313509147680883,"score_spread":0.2715882015125483,"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."}}