{"id":"W151080024","doi":"10.17705/1jais.00369","title":"Generating Effective Recommendations Using Viewing-Time Weighted Preferences for Attributes","year":2014,"lang":"en","type":"article","venue":"Journal of the Association for Information Systems","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Preference; Recommender system; Quality (philosophy); Information retrieval; Function (biology); Artificial intelligence; Mathematics; Statistics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.003458897,0.0009680244,0.001056815,0.001950762,0.0003978346,0.001590869,0.001033617,0.001127715,0.001230761],"category_scores_gemma":[0.01975723,0.0006295111,0.00119814,0.00188953,0.0002583611,0.002728094,0.0005851887,0.001173871,0.0008409771],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000536786,"about_ca_system_score_gemma":0.0005294661,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00274748,"about_ca_topic_score_gemma":0.006646233,"domain_scores_codex":[0.9974929,0.0009598921,0.0001767655,0.0005710496,0.0007111583,0.00008819463],"domain_scores_gemma":[0.987823,0.007708705,0.0007266764,0.001691507,0.001860745,0.0001894164],"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":[0.0008552772,0.0006832902,0.04509339,0.0006280289,0.0008173071,0.0002600089,0.001174001,0.09900111,0.02913481,0.01000045,0.005569669,0.8067825],"study_design_scores_gemma":[0.0001581562,0.0008755588,0.02006684,0.0001226048,0.0005782368,0.0005601046,0.0003761446,0.9278306,0.02028403,0.0219372,0.007026193,0.0001844173],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1291043,0.0009599395,0.8648897,0.0003258313,0.0001028754,0.0002429548,0.0005266158,0.001000631,0.002847259],"genre_scores_gemma":[0.5636148,0.0006774302,0.4314751,0.0001103348,0.000133486,0.0002283399,0.001059071,0.0001254451,0.002576027],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003458897,"threshold_uncertainty_score":0.01829267,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02751794013680617,"score_gpt":0.2733261143754119,"score_spread":0.2458081742386058,"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."}}