{"id":"W3213445932","doi":"10.32920/ryerson.14651604.v1","title":"A genetic algorithm approach to recommender system cold start problem","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Recommender system; MovieLens; Computer science; Cold start (automotive); Collaborative filtering; Usability; Genetic algorithm; Algorithm; Machine learning; Information retrieval; Human–computer interaction; Engineering","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":[0.0007666923,0.0005741544,0.0008678241,0.0003000539,0.0001338813,0.001483436,0.002497003,0.0004563394,0.00001260466],"category_scores_gemma":[0.000005117501,0.0005177943,0.000279218,0.0004678495,0.00001397453,0.0001950826,0.004868152,0.0006442196,0.00003515906],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004252974,"about_ca_system_score_gemma":0.0003403843,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001024407,"about_ca_topic_score_gemma":0.00002028559,"domain_scores_codex":[0.9957561,0.0003584838,0.0008908999,0.001777227,0.0005908607,0.0006264251],"domain_scores_gemma":[0.9964478,0.00004506857,0.0002767708,0.002567655,0.0003088857,0.0003538294],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000005508035,0.001447516,0.0002415856,0.006882724,0.001095554,0.0002981477,0.007045229,0.001530323,0.0001905805,0.1888981,0.4227652,0.3695996],"study_design_scores_gemma":[0.000960781,0.0004669838,0.000402787,0.003187992,0.0001142238,0.0008522297,0.002418269,0.7515765,0.004742319,0.003409168,0.2268007,0.005068053],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00004108338,0.000373231,0.9088011,0.0007546838,0.001202773,0.002049635,0.00001276768,0.001623558,0.0851412],"genre_scores_gemma":[0.02825898,0.00002895545,0.9674675,0.0007252334,0.0002291495,0.001665196,0.00001897043,0.00005368158,0.00155233],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7500462,"threshold_uncertainty_score":0.9997274,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03317269270932693,"score_gpt":0.2437074172032047,"score_spread":0.2105347244938778,"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."}}