{"id":"W2944790191","doi":"10.48550/arxiv.1905.02009","title":"Visually-aware Recommendation with Aesthetic Features","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Recommender system; Pairwise comparison; Clothing; Key (lock); Perception; Human–computer interaction; Product (mathematics); Artificial intelligence; Space (punctuation); Information retrieval; Process (computing); Dynamics (music); Machine learning; Mathematics; Psychology","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":[0.0003536676,0.0007281743,0.0006189166,0.000972536,0.0002105981,0.0006287663,0.0007606964,0.000666312,0.002011005],"category_scores_gemma":[0.001952888,0.000376775,0.0007580742,0.0008028678,0.0002948047,0.001455107,0.0006520206,0.0008940072,0.0006667417],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006963849,"about_ca_system_score_gemma":0.000339179,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01182589,"about_ca_topic_score_gemma":0.02282449,"domain_scores_codex":[0.9997434,0.00004357902,0.00001014106,0.00010152,0.00006789976,0.00003344871],"domain_scores_gemma":[0.9993326,0.0002104712,0.00008597113,0.0001185519,0.0002034398,0.00004885229],"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":[0.0006100978,0.0005622471,0.0155979,0.0004256646,0.0002487639,0.000211383,0.000334339,0.1769375,0.05924605,0.008080997,0.0123726,0.7253724],"study_design_scores_gemma":[0.00001298403,0.000076827,0.004264334,0.00001195404,0.00003118856,0.00006831121,0.00002111053,0.9878321,0.002361223,0.004350322,0.000953735,0.00001599853],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1589234,0.001530534,0.8295268,0.0005242379,0.0001006525,0.0001153981,0.0007035463,0.002315957,0.00625947],"genre_scores_gemma":[0.8630072,0.0003899405,0.1313822,0.0001583171,0.00009754282,0.00005111436,0.0007081505,0.00008102239,0.004124392],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01182589,"threshold_uncertainty_score":0.02351409,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0532979500067744,"score_gpt":0.2031261704562826,"score_spread":0.1498282204495082,"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."}}