{"id":"W1497702967","doi":"10.1007/978-3-642-01818-3_12","title":"Context Dependent Movie Recommendations Using a Hierarchical Bayesian Model","year":2009,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Context (archaeology); Set (abstract data type); Collaborative filtering; Recommender system; Preference; Context model; Bayesian probability; Maximization; Machine learning; Information retrieval; Artificial intelligence; Data mining; Mathematics; Mathematical optimization; Statistics","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":[0.003796682,0.0009215052,0.003232709,0.002001757,0.001234346,0.002331563,0.004902571,0.00333586,0.006202218],"category_scores_gemma":[0.01297746,0.002467589,0.002339564,0.003118354,0.0009473107,0.004383007,0.001415355,0.003720095,0.002342757],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00177257,"about_ca_system_score_gemma":0.001721174,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03777109,"about_ca_topic_score_gemma":0.05849943,"domain_scores_codex":[0.9974349,0.001061672,0.0001544342,0.0005907937,0.0005712361,0.0001870272],"domain_scores_gemma":[0.9927253,0.005790525,0.0002142134,0.0004899539,0.0006292708,0.0001507832],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005772574,0.0003573129,0.002412639,0.0002870348,0.000553661,0.0002429154,0.000282947,0.7785637,0.001549937,0.05082776,0.008775399,0.1555694],"study_design_scores_gemma":[0.00002767177,0.0000203997,0.0002270943,0.00001280713,0.00004810744,0.00003412939,0.000007241074,0.9894957,0.000124831,0.009557933,0.0004234357,0.00002064819],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01980218,0.001494139,0.9734396,0.0007264347,0.0001193846,0.0001294549,0.0005767891,0.0008530994,0.002858913],"genre_scores_gemma":[0.5559244,0.002281288,0.4211124,0.0005343267,0.0005436056,0.0004703736,0.001734943,0.0002765981,0.01712209],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03777109,"threshold_uncertainty_score":0.07510251,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03786400838628817,"score_gpt":0.2856890370723043,"score_spread":0.2478250286860161,"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."}}