{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005476071,0.0006187022,0.001064781,0.001083727,0.0004736829,0.0007267448,0.001078702,0.0008166283,0.001410436],"category_scores_gemma":[0.00130437,0.0003247329,0.0008788644,0.001201074,0.0001861622,0.001162778,0.0005491049,0.000657392,0.0008399732],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000436157,"about_ca_system_score_gemma":0.0007338854,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02003973,"about_ca_topic_score_gemma":0.02924405,"domain_scores_codex":[0.9994764,0.0001115275,0.00003603234,0.0001731348,0.0001430608,0.00005996992],"domain_scores_gemma":[0.9994165,0.0001547051,0.00004690401,0.0000956153,0.0002513148,0.00003504663],"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":[0.0006783986,0.0008227198,0.01896178,0.0004448728,0.000692118,0.0006610121,0.0003533405,0.2263114,0.04104174,0.007522083,0.01257339,0.6899371],"study_design_scores_gemma":[0.00002277582,0.00008813166,0.001873164,0.00001000439,0.00007050055,0.0001590045,0.00004166552,0.9927166,0.002448941,0.001014812,0.001530694,0.00002375739],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1010796,0.001826647,0.8892447,0.0003286241,0.0001443612,0.0001390349,0.0003636102,0.002700823,0.004172574],"genre_scores_gemma":[0.6509386,0.0007123908,0.342078,0.0002410875,0.0001302787,0.0001214107,0.0006760125,0.00006601824,0.005036192],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02003973,"threshold_uncertainty_score":0.03984618,"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."}}