{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003731676,0.0008641104,0.0008365733,0.001813504,0.0005985789,0.001388102,0.001033385,0.001082348,0.001305292],"category_scores_gemma":[0.0111917,0.0004439828,0.001101142,0.002030127,0.0002817272,0.002259812,0.0006257402,0.001466337,0.001127983],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009556177,"about_ca_system_score_gemma":0.0008583427,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01283771,"about_ca_topic_score_gemma":0.02440975,"domain_scores_codex":[0.9969938,0.001711782,0.0001653347,0.0004069704,0.0005658306,0.000156282],"domain_scores_gemma":[0.9934596,0.003682474,0.0004608227,0.001185117,0.000960513,0.0002515652],"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":[0.001339152,0.00128244,0.09395788,0.0006326791,0.000897712,0.0004398015,0.001831138,0.3186878,0.01965358,0.03736908,0.01462414,0.5092846],"study_design_scores_gemma":[0.00001417664,0.00009208793,0.004762718,0.00002030712,0.00005674325,0.000162421,0.0001115776,0.9840534,0.00158676,0.006362156,0.002739932,0.00003769354],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1315245,0.0007660505,0.8608156,0.0007232956,0.00006154059,0.0002279665,0.001172479,0.001331827,0.003376777],"genre_scores_gemma":[0.7764155,0.0004009339,0.2190674,0.0001614919,0.00005249092,0.0001338624,0.001317593,0.00008796025,0.00236277],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01283771,"threshold_uncertainty_score":0.02552599,"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."}}