{"id":"W2021863237","doi":"10.1145/2630099.2630100","title":"Collaborative filtering of color aesthetics","year":2014,"lang":"en","type":"article","venue":"","topic":"Color perception and design","field":"Psychology","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Collaborative filtering; Artificial intelligence; Set (abstract data type); Variation (astronomy); Space (punctuation); Probabilistic logic; Similarity (geometry); Feature (linguistics); Matrix decomposition; Theme (computing); Matrix (chemical analysis); Feature vector; Machine learning; Recommender system; Eigenvalues and eigenvectors; Image (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":[0.002771975,0.0006298579,0.0009774226,0.0018313,0.0005913306,0.001431606,0.0008625347,0.0007770801,0.00248694],"category_scores_gemma":[0.01261716,0.0003225844,0.001159069,0.00143998,0.0004506542,0.001678855,0.0006468056,0.001058176,0.0007172812],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001231917,"about_ca_system_score_gemma":0.0004750553,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009319901,"about_ca_topic_score_gemma":0.01156953,"domain_scores_codex":[0.9980987,0.0005354895,0.00006562322,0.0005866243,0.0005882655,0.0001252629],"domain_scores_gemma":[0.9934524,0.003694227,0.0005659265,0.001011487,0.001084124,0.0001917906],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00154096,0.001007761,0.0800209,0.0004524472,0.0006941123,0.0003729333,0.001657734,0.1549907,0.03616047,0.01940077,0.01210777,0.6915933],"study_design_scores_gemma":[0.00003537814,0.0002894948,0.02919291,0.00003024443,0.000101075,0.0003111934,0.0002477128,0.9414186,0.006981532,0.01644889,0.004865273,0.00007767668],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3962155,0.00114525,0.5903081,0.0006778899,0.0001160902,0.0001279469,0.0007599975,0.001471749,0.009177407],"genre_scores_gemma":[0.9464985,0.0002292017,0.0493383,0.000084541,0.00005467672,0.00004524019,0.0004695372,0.00006534081,0.00321461],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009319901,"threshold_uncertainty_score":0.01853132,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02061189292305778,"score_gpt":0.3189724283351512,"score_spread":0.2983605354120934,"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."}}