{"id":"W4235533045","doi":"10.32920/ryerson.14651604","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; Data mining; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003831992,0.001076198,0.001935916,0.001523391,0.001216467,0.001575168,0.002622488,0.002827695,0.002080051],"category_scores_gemma":[0.009439099,0.000681004,0.001166387,0.001844808,0.001293918,0.001668472,0.00103613,0.002374579,0.0005663902],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00156024,"about_ca_system_score_gemma":0.002252129,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01759712,"about_ca_topic_score_gemma":0.01255958,"domain_scores_codex":[0.9969652,0.001497773,0.0001425551,0.0005596063,0.0006196856,0.0002152662],"domain_scores_gemma":[0.9934784,0.004227037,0.0002983086,0.0004634235,0.001379478,0.0001532961],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001209089,0.0001657042,0.002166967,0.0001190179,0.0002495413,0.0001353662,0.000209727,0.8887274,0.001319803,0.01805659,0.002903893,0.08582515],"study_design_scores_gemma":[0.00004041611,0.00006404025,0.0003212783,0.00001390209,0.00003405722,0.00005588439,0.00002737632,0.9906886,0.0004378851,0.007061428,0.001238793,0.00001627281],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0259567,0.0008410095,0.9683725,0.0006846435,0.0001025102,0.0001708725,0.0001013681,0.0003557708,0.003414701],"genre_scores_gemma":[0.4712513,0.0007703289,0.5192407,0.0005752923,0.0001873189,0.0004592294,0.0004310022,0.0000989705,0.006985907],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01759712,"threshold_uncertainty_score":0.03498936,"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."}}