{"id":"W4391637045","doi":"10.32920/25191011","title":"Movie Recommendation using Multiple Data Sources","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"MovieLens; Recommender system; Computer science; Collaborative filtering; Trailer; Information retrieval; Sentiment analysis; Social media; World Wide Web; Artificial intelligence","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":[0.005748167,0.001958071,0.002524263,0.0051956,0.001451557,0.004393953,0.002383338,0.002069953,0.003122363],"category_scores_gemma":[0.02240948,0.001410954,0.003213779,0.007584111,0.0004494992,0.006680145,0.002084497,0.002541457,0.002416297],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001517581,"about_ca_system_score_gemma":0.001411993,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04918137,"about_ca_topic_score_gemma":0.0658307,"domain_scores_codex":[0.9917461,0.002654498,0.0006816283,0.001891738,0.002762312,0.0002636552],"domain_scores_gemma":[0.9802649,0.00908834,0.0009452506,0.004094176,0.005064155,0.0005432203],"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":[0.00181692,0.001313003,0.05212045,0.001811834,0.003619204,0.001323837,0.000817896,0.1347479,0.01274512,0.01000464,0.03096522,0.748714],"study_design_scores_gemma":[0.0001869519,0.0004137529,0.011739,0.0001643653,0.000740318,0.0003780612,0.0003944243,0.9633875,0.005472275,0.005822419,0.01111963,0.0001812643],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1750772,0.01080925,0.7688544,0.004777056,0.0008000734,0.00148065,0.01095616,0.007138338,0.02010679],"genre_scores_gemma":[0.6140458,0.002179913,0.3619512,0.00064745,0.0003612842,0.000396304,0.0115877,0.0001733054,0.008657034],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04918137,"threshold_uncertainty_score":0.09779018,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1996696381138619,"score_gpt":0.3585002836301892,"score_spread":0.1588306455163272,"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."}}