{"id":"W2911488506","doi":"10.1016/j.cosrev.2019.01.001","title":"Progress in context-aware recommender systems — An overview","year":2019,"lang":"en","type":"article","venue":"Computer Science Review","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":150,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Recommender system; Computer science; Leverage (statistics); Context (archaeology); Process (computing); Collaborative filtering; Limiting; Domain (mathematical analysis); World Wide Web; Information retrieval; Data science; 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":[0.005225454,0.001041108,0.002350225,0.003375085,0.0006493835,0.002587236,0.001941634,0.002492936,0.002700646],"category_scores_gemma":[0.008409239,0.0007946289,0.001013365,0.007177229,0.0007509359,0.005602803,0.001432424,0.003194755,0.001696212],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001306922,"about_ca_system_score_gemma":0.002643131,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003886754,"about_ca_topic_score_gemma":0.003774569,"domain_scores_codex":[0.9978197,0.000520924,0.0002352223,0.0005184168,0.0007788339,0.0001269926],"domain_scores_gemma":[0.9890835,0.007353041,0.0004171116,0.0004074477,0.00237725,0.0003616007],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001252847,0.0001679444,0.00141466,0.01183251,0.000211171,0.00007041856,0.0001701986,0.001920666,0.001515177,0.01447555,0.02161456,0.9464818],"study_design_scores_gemma":[0.00007074155,0.0006873945,0.00465423,0.006126681,0.0008585085,0.001843955,0.0004603823,0.01153058,0.002614292,0.02235193,0.9485996,0.0002016949],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0009376817,0.9885691,0.007127614,0.001300339,0.0003558212,0.00001906548,0.00004975314,0.00005622027,0.001584348],"genre_scores_gemma":[0.01046915,0.9703639,0.01580632,0.0007557254,0.001702336,0.00003174146,0.0001407491,0.00001647093,0.0007137239],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.005225454,"threshold_uncertainty_score":0.02763516,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0757979117103746,"score_gpt":0.3502193773659615,"score_spread":0.2744214656555869,"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."}}