{"id":"W1867270225","doi":"10.1142/s0219622015500194","title":"Modeling Tag-Aware Recommendations Based on User Preferences","year":2015,"lang":"en","type":"article","venue":"International Journal of Information Technology & Decision Making","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"National Natural Science Foundation of China; National Science Foundation","keywords":"Recommender system; Computer science; Popularity; Similarity (geometry); Information retrieval; Relation (database); 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009813773,0.0001297069,0.0001871945,0.001988187,0.00007618469,0.0003614985,0.001850121,0.0001511443,0.00001873394],"category_scores_gemma":[0.00059307,0.0001050067,0.00008998589,0.0004229782,0.00002038125,0.002732277,0.0002307049,0.0003277216,0.00003634261],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002041524,"about_ca_system_score_gemma":0.0001872191,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004099938,"about_ca_topic_score_gemma":0.000002811177,"domain_scores_codex":[0.9978833,0.00004275178,0.0009923931,0.0001138729,0.0008285265,0.000139111],"domain_scores_gemma":[0.9968448,0.0001230642,0.0009320523,0.0003014396,0.001736304,0.000062288],"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":[0.00006768473,0.00006972982,0.0009209883,0.0000033699,0.00005062755,0.00001403213,0.0003512776,0.04383227,0.00000601127,0.07286035,0.01034816,0.8714755],"study_design_scores_gemma":[0.000713777,0.0002338386,0.00005202795,0.0005528791,0.00000442602,0.0001827388,0.0003817702,0.8902155,0.0003962319,0.08220452,0.02490723,0.0001551251],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004128355,0.00002542334,0.9873071,0.005085604,0.001726972,0.00009087905,0.000003963544,0.0001641831,0.001467533],"genre_scores_gemma":[0.7314889,0.0000138812,0.2677648,0.0006645026,0.00005046857,0.000006671605,0.000002871904,0.000003718878,0.000004108228],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8713204,"threshold_uncertainty_score":0.4282048,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04210268528459719,"score_gpt":0.3354173869547105,"score_spread":0.2933147016701133,"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."}}