{"id":"W4280534985","doi":"10.18280/ria.360203","title":"Toward Preference and Context-Aware Hybrid Tourist Recommender System Based on Machine Learning Techniques","year":2022,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Digital Marketing and Social Media","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Recommender system; Machine learning; Collaborative filtering; Artificial intelligence; Context (archaeology); Naive Bayes classifier; Random forest; Precision and recall; Set (abstract data type); Field (mathematics); Tourism; Preference; Preference learning; Data mining; Information retrieval; Support vector machine; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.001686312,0.0001513986,0.0002253465,0.0001024899,0.001377731,0.0001515628,0.000323464,0.00005004392,0.0003934613],"category_scores_gemma":[0.0005856038,0.0001642055,0.00007626562,0.0002886314,0.0001926239,0.00009838703,0.0001234251,0.0004489311,0.00003261916],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002510476,"about_ca_system_score_gemma":0.00009939342,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001129714,"about_ca_topic_score_gemma":0.0001093513,"domain_scores_codex":[0.9979378,0.0006444712,0.0003031293,0.0003835281,0.0003690596,0.0003620206],"domain_scores_gemma":[0.9987569,0.0006855981,0.0001425313,0.000187625,0.00007990182,0.0001473814],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003096758,0.0005041813,0.00571032,0.0003512774,0.00003820501,0.0001448027,0.04065949,0.00340107,0.0001028347,0.03541919,0.004829671,0.9085293],"study_design_scores_gemma":[0.0001297305,0.0008969786,0.00005739429,0.0005176439,0.00003253471,0.00001827495,0.1576545,0.1370023,0.00562477,0.001417552,0.6957831,0.0008652102],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1787883,0.001927129,0.04027113,0.02130746,0.003643675,0.00340486,0.0002988257,0.003456673,0.7469019],"genre_scores_gemma":[0.9956769,0.00008188054,0.0001270035,0.0003099363,0.0001273136,0.0001201156,0.00001818569,0.00002063837,0.003517968],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9076641,"threshold_uncertainty_score":0.9999223,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07071925693786518,"score_gpt":0.2853002013634047,"score_spread":0.2145809444255395,"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."}}