{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001358515,0.001014793,0.001634804,0.001654386,0.001361626,0.001182569,0.00196685,0.001409186,0.001829935],"category_scores_gemma":[0.004245289,0.000596295,0.00119271,0.002234933,0.0004399878,0.002119239,0.001398249,0.001180805,0.001556277],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008079677,"about_ca_system_score_gemma":0.00120236,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0146333,"about_ca_topic_score_gemma":0.01356869,"domain_scores_codex":[0.9978253,0.00043893,0.0001577943,0.0006653027,0.0007649901,0.00014773],"domain_scores_gemma":[0.9972605,0.0005766226,0.0002211559,0.0007518471,0.001084286,0.0001056024],"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":[0.0003464773,0.000273348,0.003624788,0.000456876,0.0005782202,0.0002453103,0.0003724984,0.3922363,0.02594597,0.01632852,0.01015169,0.54944],"study_design_scores_gemma":[0.00003397869,0.0001203946,0.0009322019,0.0000192178,0.00007826037,0.0001433688,0.00008854936,0.9806685,0.007436493,0.005854936,0.004560866,0.00006319509],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01645515,0.000529104,0.9778765,0.0001247,0.00007845821,0.0001168645,0.0001185021,0.002308725,0.002391974],"genre_scores_gemma":[0.531118,0.0006747446,0.4622487,0.0002072202,0.0001352938,0.000250445,0.0007022255,0.0001683243,0.004495081],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0146333,"threshold_uncertainty_score":0.02909625,"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."}}