{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002458964,0.0001697929,0.0003392482,0.0003504629,0.0001252054,0.0001342368,0.002275645,0.00007735258,0.0001079239],"category_scores_gemma":[0.00002861985,0.00011652,0.0001794781,0.0006481597,0.00003565233,0.002727094,0.0001020782,0.00006304806,0.00001003778],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005117532,"about_ca_system_score_gemma":0.00004669107,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002623748,"about_ca_topic_score_gemma":0.0008108974,"domain_scores_codex":[0.9985726,0.00008040207,0.0004023005,0.0006417609,0.0001181798,0.0001847375],"domain_scores_gemma":[0.9963124,0.0007535935,0.0001765218,0.002617705,0.00008801372,0.00005172679],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004939041,0.0002957832,0.0012294,0.00003441428,0.001733833,2.755784e-7,0.0003939413,0.0000417689,0.002664299,0.001481985,0.003562809,0.9885121],"study_design_scores_gemma":[0.007133161,0.001898118,0.05084994,0.0008072035,0.00516641,0.000007127373,0.000639175,0.1621597,0.1757234,0.02592453,0.5662169,0.003474256],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0150475,0.00007162162,0.9777194,0.000347857,0.0005684756,0.000357523,0.005647428,0.0001263437,0.0001138559],"genre_scores_gemma":[0.9662873,0.00007953702,0.03199461,0.00003311884,0.00006105348,0.0001602973,0.0008113271,0.00001431124,0.0005584374],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9850379,"threshold_uncertainty_score":0.4751548,"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."}}