{"id":"W2109720450","doi":"10.1145/1015330.1015437","title":"The multiple multiplicative factor model for collaborative filtering","year":2004,"lang":"en","type":"article","venue":"","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":90,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Latent variable; Computer science; Multiplicative function; Semantics (computer science); Factor (programming language); Feature vector; Latent variable model; Expression (computer science); Data modeling; Latent class model; Feature (linguistics); Artificial intelligence; Generative model; Binary number; Data mining; Machine learning; Generative grammar; 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":[0.004784672,0.001735102,0.001726383,0.001877008,0.001023136,0.002469973,0.003742562,0.002899546,0.00763918],"category_scores_gemma":[0.0148763,0.0008485229,0.002318008,0.00309915,0.001844094,0.004496198,0.001657704,0.002893639,0.003054754],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00175314,"about_ca_system_score_gemma":0.001409746,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009050409,"about_ca_topic_score_gemma":0.007767902,"domain_scores_codex":[0.9957772,0.002111043,0.0001865069,0.0008790354,0.0008249143,0.0002212823],"domain_scores_gemma":[0.9946902,0.003581164,0.000375677,0.0006505794,0.0005788603,0.0001235063],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007935637,0.00009000576,0.001719739,0.0003083522,0.0002825889,0.0001934459,0.0003836481,0.1946978,0.0009052067,0.7007011,0.006707477,0.09393132],"study_design_scores_gemma":[0.00003095167,0.00006497107,0.0004033559,0.00004913164,0.00008035122,0.0001970947,0.00003756846,0.5864472,0.0002668645,0.3986405,0.01373265,0.00004929383],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001724011,0.0007036903,0.9938444,0.0004447171,0.00008234019,0.00004292359,0.0001794759,0.0001656408,0.002812768],"genre_scores_gemma":[0.3364991,0.004535907,0.6338061,0.000838162,0.0009165957,0.0009231805,0.001296836,0.0001855114,0.02099863],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009050409,"threshold_uncertainty_score":0.02555555,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05443365448484967,"score_gpt":0.2880791299353352,"score_spread":0.2336454754504856,"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."}}