{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000412444,0.000332593,0.0003892598,0.0002435933,0.0002289211,0.0003348177,0.001127982,0.0004364459,0.00002917391],"category_scores_gemma":[0.00007873069,0.000370499,0.0001795486,0.0003333997,0.00005769525,0.00050788,0.001284077,0.0003893986,0.000008096158],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001796389,"about_ca_system_score_gemma":0.0001062804,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000819017,"about_ca_topic_score_gemma":0.00001615749,"domain_scores_codex":[0.9980863,0.0001092495,0.0002677522,0.001135447,0.0000613508,0.0003398625],"domain_scores_gemma":[0.9980296,0.0003540061,0.0004099227,0.000915046,0.0001416776,0.0001497181],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00009152368,0.0003103903,0.005088241,0.0006264991,0.0004129196,0.00006528092,0.001454633,0.0005498506,0.00007964269,0.8433177,0.02718523,0.1208181],"study_design_scores_gemma":[0.0005751411,0.0002487556,0.0007832781,0.0002575751,0.00006425816,0.00001739847,0.00003556606,0.4381669,0.000446428,0.5144233,0.04416764,0.000813722],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04515238,0.0000741047,0.9507751,0.0004982019,0.001089309,0.0006971989,0.00003948047,0.0003465315,0.001327649],"genre_scores_gemma":[0.9424129,0.0005196451,0.05616893,0.000316173,0.0002617131,0.00001021707,0.0000340649,0.00002532961,0.0002510634],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8972605,"threshold_uncertainty_score":0.9998747,"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."}}