{"id":"W4321151143","doi":"10.1016/j.compeleceng.2023.108622","title":"Dynamic context management in context-aware recommender systems","year":2023,"lang":"en","type":"article","venue":"Computers & Electrical Engineering","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":false,"ca_institutions":"Dalhousie University","funders":"Sichuan Province Science and Technology Support Program; National Natural Science Foundation of China","keywords":"Recommender system; Computer science; Exploit; Context (archaeology); Information retrieval; Identification (biology); Context model; Machine learning; Artificial intelligence; Data mining; Computer security","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"],"consensus_categories":[],"category_scores_codex":[0.0004122955,0.0002737637,0.0004127937,0.0007373336,0.00006105968,0.0002277364,0.0009532401,0.0001089228,0.00000134908],"category_scores_gemma":[0.000009818533,0.0002790211,0.00009919237,0.001737707,0.000008301622,0.0002730897,0.0003574167,0.0003181691,0.00005594793],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003158167,"about_ca_system_score_gemma":0.00001769831,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006539373,"about_ca_topic_score_gemma":0.000006740599,"domain_scores_codex":[0.9979467,0.0000665563,0.0004850875,0.0005505807,0.0002625379,0.0006885691],"domain_scores_gemma":[0.9990411,0.0002039402,0.00007043619,0.0005143336,0.00003381614,0.0001363973],"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":[0.000008523122,0.0001325518,0.0002558944,0.0003148525,0.0002294907,0.0005733187,0.0007404371,0.02677665,0.0001543141,0.1585519,0.0334745,0.7787876],"study_design_scores_gemma":[0.0004022803,0.00006127457,0.0009107979,0.0001420792,0.000003246106,0.00002810126,0.0000348705,0.9757514,0.00004511035,0.0001875463,0.02211667,0.0003166322],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002254943,0.0005624361,0.9924123,0.0006841689,0.001398097,0.0005174182,0.000001003216,0.001863057,0.000306571],"genre_scores_gemma":[0.9923663,0.0001293504,0.006880813,0.0002303631,0.00004071101,0.0001502231,0.00000693319,0.00003339281,0.0001619097],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9901114,"threshold_uncertainty_score":0.9999662,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01293554215777013,"score_gpt":0.2312096448532433,"score_spread":0.2182741026954731,"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."}}