{"id":"W3200930594","doi":"10.32920/ryerson.14655801.v1","title":"Development of personalized online systems for web search, recommendations, and e-commerce","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University; Toronto Metropolitan University","funders":"","keywords":"Computer science; World Wide Web; Web page; Personalization; Information retrieval; Web navigation; Web modeling; Web service; Personalized search; Static web page; Context (archaeology); Data Web; Web design; Web mining","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":[0.002027864,0.0007879857,0.001124554,0.001923253,0.0009382917,0.002076861,0.002127152,0.001866328,0.005327011],"category_scores_gemma":[0.004896534,0.0009155527,0.0009594008,0.002215924,0.0009218661,0.004831546,0.001687461,0.002696896,0.003813101],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001119466,"about_ca_system_score_gemma":0.001778274,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003807535,"about_ca_topic_score_gemma":0.003472383,"domain_scores_codex":[0.9983264,0.000360904,0.000112799,0.0002842825,0.0008258056,0.00008979278],"domain_scores_gemma":[0.9977488,0.0005915711,0.0001205012,0.0005406933,0.0008706963,0.0001278393],"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":[0.0000837987,0.0003214591,0.001565486,0.0005516623,0.0001257764,0.0002057446,0.0004352877,0.01540705,0.01257126,0.1750207,0.02140379,0.7723081],"study_design_scores_gemma":[0.0001085779,0.0007507924,0.003498257,0.0004435128,0.000252128,0.000976697,0.0003097307,0.4299123,0.02077229,0.09852327,0.444234,0.0002184554],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006657059,0.004994521,0.965403,0.0009610221,0.0004147901,0.0005952211,0.0002277625,0.004212074,0.01653454],"genre_scores_gemma":[0.07073272,0.006413695,0.9009473,0.0006256819,0.000376952,0.0006698224,0.0007324939,0.0002450802,0.01925629],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005327011,"threshold_uncertainty_score":0.01782066,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08874970916079945,"score_gpt":0.3352120106994605,"score_spread":0.2464623015386611,"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."}}