{"id":"W2803782109","doi":"10.1007/s10791-018-9332-3","title":"(CF)2 architecture: contextual collaborative filtering","year":2018,"lang":"en","type":"article","venue":"Information Retrieval","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Oakville-Trafalgar Memorial Hospital; Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Collaborative filtering; Computer science; Recommender system; Context (archaeology); Architecture; The Internet; Selection (genetic algorithm); Fraction (chemistry); Machine learning; Scale (ratio); Artificial intelligence; Contextual design; Filter (signal processing); Information retrieval; Data science; World Wide Web; Human–computer interaction","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002917208,0.0001008674,0.0001146189,0.0001404244,0.0001422685,0.0003434375,0.0004318013,0.0000617633,0.00003299108],"category_scores_gemma":[0.00007161121,0.0000858862,0.00003260127,0.000501385,0.00005006672,0.001780584,0.000148298,0.00009592235,0.0002163107],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004030928,"about_ca_system_score_gemma":0.00006809886,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001259881,"about_ca_topic_score_gemma":0.000004886374,"domain_scores_codex":[0.999128,0.00004046856,0.0003115129,0.0001032489,0.0002434885,0.0001733191],"domain_scores_gemma":[0.9991351,0.00004205893,0.000154798,0.0003025818,0.0003036635,0.00006178449],"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":[0.0001540137,0.00004381876,0.0003142883,0.00007599805,0.00009018445,0.000005344052,0.03522752,0.00001831054,0.002790363,0.2796016,0.05645462,0.6252239],"study_design_scores_gemma":[0.0006616793,0.000732184,0.0008712409,0.00004955779,0.000002952451,0.00006345531,0.0003094287,0.009323559,0.1025864,0.00323473,0.8818018,0.0003630052],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00581958,0.00001270129,0.9598922,0.0006531852,0.0006009172,0.000235338,0.000008559334,0.0003860067,0.0323915],"genre_scores_gemma":[0.9643563,0.000003070033,0.03435906,0.000993046,0.00016344,0.00000846122,0.000007479706,0.000003849068,0.0001052965],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9585367,"threshold_uncertainty_score":0.3502337,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01103614124488447,"score_gpt":0.2479555254115812,"score_spread":0.2369193841666967,"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."}}