{"id":"W4241467072","doi":"10.32920/ryerson.14655609.v1","title":"Modeling User's Non-Functional Preferences for Personalized Service Ranking","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Ranking (information retrieval); Web service; Context (archaeology); User modeling; Task (project management); Service (business); World Wide Web; Selection (genetic algorithm); Process (computing); Information retrieval; Human–computer interaction; User interface; Machine learning; 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"],"consensus_categories":[],"category_scores_codex":[0.000560887,0.000315354,0.000469338,0.0001266532,0.0001751181,0.0009869664,0.001193081,0.0002992594,0.00004690373],"category_scores_gemma":[0.00001488946,0.0002767616,0.0002783552,0.00016585,0.000008684683,0.0003910868,0.001404019,0.0003441155,0.000003518348],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007256446,"about_ca_system_score_gemma":0.0003920114,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001251464,"about_ca_topic_score_gemma":0.0003173012,"domain_scores_codex":[0.9978561,0.00007306842,0.0004477933,0.0009348142,0.0003887515,0.0002995146],"domain_scores_gemma":[0.9984956,0.000101231,0.0001503839,0.0007524291,0.0004219866,0.00007836128],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002605347,0.001408525,0.003008506,0.01347271,0.004460598,0.00006517071,0.03660993,0.1965789,0.003246743,0.5431023,0.04639624,0.1513898],"study_design_scores_gemma":[0.000307186,0.00001874901,0.00003466603,0.0003865289,0.00001810698,0.00001033396,0.0001968471,0.9830014,0.0004859141,0.01315455,0.001996532,0.0003891995],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006391584,0.0002883007,0.9867651,0.001167611,0.001607321,0.0006962155,0.000005939838,0.0004921024,0.002585846],"genre_scores_gemma":[0.5227672,0.00005352212,0.4742104,0.0009280907,0.0003286909,0.0007051244,0.00009064061,0.00002474175,0.0008915837],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7864224,"threshold_uncertainty_score":0.9999685,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08866700304233838,"score_gpt":0.2895209231249343,"score_spread":0.2008539200825959,"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."}}