{"id":"W4280513273","doi":"10.1109/syscon53536.2022.9773925","title":"Context-Aware Recommendation Systems Using Consensus-Clustering","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Systems Conference (SysCon)","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Recommender system; Computer science; Cluster analysis; Scalability; Collaborative filtering; Data mining; Context (archaeology); Information overload; RSS; Machine learning; Bipartite graph; Artificial intelligence; Graph; Theoretical computer science; World Wide Web; Database","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":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001474479,0.0003856609,0.0005602337,0.0004780555,0.0006413352,0.0010758,0.001965272,0.0001135811,0.0003475707],"category_scores_gemma":[0.00002920228,0.0004177996,0.0001602302,0.0003918556,0.00004967324,0.0006883633,0.0008448166,0.0005047744,0.00004790249],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009637156,"about_ca_system_score_gemma":0.0002712867,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004417729,"about_ca_topic_score_gemma":0.00009038951,"domain_scores_codex":[0.9957387,0.0007686428,0.00115305,0.0008905687,0.0009554744,0.0004935405],"domain_scores_gemma":[0.9975844,0.0002157028,0.000861152,0.000702058,0.0004742491,0.0001624399],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002471265,0.001064866,0.01447739,0.001070655,0.002443109,0.0008126234,0.008354697,0.05403463,0.03276318,0.7048765,0.09999645,0.07985873],"study_design_scores_gemma":[0.0005878049,0.000120508,0.00006650019,0.0001912905,0.0000127282,0.0009239404,0.002561051,0.8983077,0.0003215624,0.000190087,0.09617263,0.000544222],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01816953,0.0002855613,0.9428908,0.001751427,0.02744204,0.001317072,0.0002516261,0.0007245392,0.007167398],"genre_scores_gemma":[0.9957182,0.00001645157,0.0007132817,0.0002437831,0.0004318727,0.0005365662,0.00007393519,0.00003959812,0.002226359],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9775486,"threshold_uncertainty_score":0.9999612,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09034234274947438,"score_gpt":0.307696837563617,"score_spread":0.2173544948141426,"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."}}