{"id":"W3149990912","doi":"10.1109/asonam49781.2020.9381349","title":"Movie Recommendation using YouTube Movie Trailer Data as the Side Information","year":2020,"lang":"en","type":"article","venue":"","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Trailer; Computer science; Recommender system; Matrix decomposition; Artificial intelligence; Sentiment analysis; Matrix (chemical analysis); Information retrieval; Film genre; Machine learning; Movie theater","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":[0.0004625567,0.0001118616,0.0001176852,0.00004141846,0.0001575733,0.0004668641,0.001400485,0.00004767135,0.00008564537],"category_scores_gemma":[0.00006709024,0.00007287024,0.0000328266,0.0002894223,0.00001266541,0.004140469,0.0006850783,0.0001218724,0.0001171773],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002678913,"about_ca_system_score_gemma":0.00006088888,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004061811,"about_ca_topic_score_gemma":0.00002009274,"domain_scores_codex":[0.9989918,0.00008606768,0.0003509324,0.0002280086,0.0001836126,0.0001595255],"domain_scores_gemma":[0.9989315,0.00005523336,0.0001410097,0.0007551452,0.00005676469,0.0000603501],"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":[0.000005246716,0.00002244523,0.0001462911,0.0000396811,0.00003796604,0.000001598285,0.004199656,0.00005146528,0.0003306435,0.03713061,0.1034967,0.8545377],"study_design_scores_gemma":[0.0001392981,0.00004278849,0.0001345843,0.00001273944,0.000006132825,0.00001738793,0.000307213,0.5320128,0.001135763,0.001085475,0.4649484,0.0001573295],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0005277677,0.00001775896,0.9360833,0.03496984,0.0002359941,0.000299236,0.00001023897,0.0003544591,0.02750144],"genre_scores_gemma":[0.812407,0.00005609498,0.1442225,0.04266315,0.0003052263,0.00002626414,0.0002068985,0.00001617303,0.00009661304],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8543804,"threshold_uncertainty_score":0.4501981,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1521784443062174,"score_gpt":0.3117383849781403,"score_spread":0.1595599406719229,"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."}}