{"id":"W4299797629","doi":"10.48550/arxiv.1803.00146","title":"A Generic Top-N Recommendation Framework For Trading-off Accuracy,\\n Novelty, and Coverage","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Institute for Computing, Information and Cognitive Systems","keywords":"Novelty; Ranking (information retrieval); Computer science; Personalization; Recommender system; Key (lock); Collaborative filtering; Revenue; Information retrieval; Space (punctuation); Data mining; Data science; World Wide Web; Computer security; Business","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003164843,0.001494529,0.002327243,0.002120524,0.001507643,0.002101853,0.004439317,0.002453782,0.004075348],"category_scores_gemma":[0.007694246,0.0007154912,0.001430728,0.003558954,0.0008127042,0.00363194,0.002001969,0.002033572,0.002381897],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001529419,"about_ca_system_score_gemma":0.002118213,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01154679,"about_ca_topic_score_gemma":0.02978427,"domain_scores_codex":[0.9972808,0.0006776934,0.0001711494,0.0007220537,0.0009610123,0.0001871005],"domain_scores_gemma":[0.9969745,0.0006823057,0.0002409729,0.00122192,0.0006937474,0.0001865178],"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":[0.0003083006,0.0007023132,0.004743787,0.0006202229,0.0003733399,0.0003146362,0.0002911761,0.2429144,0.01342003,0.0546768,0.03021333,0.6514217],"study_design_scores_gemma":[0.0000492682,0.0001552051,0.000685318,0.00002871901,0.00008155608,0.0003675076,0.00003872549,0.9670702,0.002519372,0.01803533,0.01091302,0.0000558178],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005489449,0.0005928426,0.9891192,0.0002457288,0.00005855073,0.0002042152,0.0003369594,0.001587701,0.002365347],"genre_scores_gemma":[0.1774739,0.0007382852,0.8122259,0.0002480251,0.0002781772,0.0002651153,0.0007076307,0.0001795378,0.007883463],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01154679,"threshold_uncertainty_score":0.02295917,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1115721594499727,"score_gpt":0.2363809871416944,"score_spread":0.1248088276917217,"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."}}