{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001217673,0.001085123,0.001180602,0.002046117,0.0005703015,0.0008005527,0.0007320299,0.0008399513,0.002010707],"category_scores_gemma":[0.004843639,0.000473217,0.0007326895,0.00204222,0.0001409175,0.002044125,0.0005418363,0.001014501,0.001729694],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005251156,"about_ca_system_score_gemma":0.0005713247,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04876405,"about_ca_topic_score_gemma":0.1121813,"domain_scores_codex":[0.999143,0.000150744,0.00007586348,0.0002662697,0.0002948174,0.00006929188],"domain_scores_gemma":[0.997919,0.0005853812,0.0001594133,0.0004140566,0.0007968643,0.0001251646],"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":[0.001998771,0.001058411,0.06329732,0.001178004,0.001002882,0.0007256205,0.0004762863,0.0301102,0.07823727,0.001640309,0.04439367,0.7758813],"study_design_scores_gemma":[0.0001286268,0.0007700584,0.04553979,0.0001320673,0.0003749942,0.0004507996,0.0001799612,0.9025612,0.03198095,0.001647276,0.01606236,0.0001718949],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5386268,0.005715609,0.3763916,0.001993954,0.0008239573,0.001423525,0.02612354,0.02559973,0.02330121],"genre_scores_gemma":[0.7192094,0.001269953,0.2491032,0.0003692893,0.0002936774,0.0002117913,0.01792857,0.0001957665,0.01141832],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04876405,"threshold_uncertainty_score":0.09696043,"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."}}