{"id":"W2145142319","doi":"10.1145/1639714.1639770","title":"Harnessing the power of \"favorites\" lists for recommendation systems","year":2009,"lang":"en","type":"article","venue":"","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Collaborative filtering; Computer science; Information retrieval; Recommender system; Set (abstract data type); Amazon rainforest; Quality (philosophy); Measure (data warehouse); Bayesian probability; World Wide Web; Data mining; 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":[0.005377457,0.001117397,0.002182493,0.006393011,0.001712486,0.003430877,0.002477365,0.002089999,0.002096345],"category_scores_gemma":[0.04239788,0.00154534,0.00126878,0.005236879,0.001336747,0.008310408,0.0024932,0.001786814,0.001479987],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007767706,"about_ca_system_score_gemma":0.0008271608,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006066794,"about_ca_topic_score_gemma":0.01042383,"domain_scores_codex":[0.9928525,0.002349071,0.0004056023,0.001296695,0.002855583,0.0002405373],"domain_scores_gemma":[0.9528939,0.03355312,0.003361268,0.005219657,0.004431625,0.0005405009],"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":[0.0008425969,0.0006017947,0.04307109,0.001304877,0.001092663,0.0006210257,0.002799561,0.1044155,0.02913651,0.03968754,0.005418274,0.7710085],"study_design_scores_gemma":[0.00009385863,0.0005499118,0.0124132,0.0001161502,0.0003904102,0.0009944558,0.0002772442,0.89926,0.01254989,0.06282806,0.01030724,0.0002196347],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06993663,0.002931411,0.9196542,0.0006751939,0.00006197863,0.0001218664,0.0003938968,0.001608667,0.004616098],"genre_scores_gemma":[0.7172874,0.001161764,0.2774735,0.0002291269,0.0004377563,0.000145891,0.0005937065,0.0001740235,0.002496846],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006393011,"threshold_uncertainty_score":0.02843904,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03013406483753283,"score_gpt":0.2888685423191891,"score_spread":0.2587344774816563,"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."}}