{"id":"W3082147375","doi":"10.3390/ijgi9090519","title":"A Social–Aware Recommender System Based on User’s Personal Smart Devices","year":2020,"lang":"en","type":"article","venue":"ISPRS International Journal of Geo-Information","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"K.N.Toosi University of Technology; University of Calgary","keywords":"RSS; Collaborative filtering; Computer science; Recommender system; Personalization; Cold start (automotive); Cluster analysis; Similarity (geometry); Context (archaeology); Information retrieval; Data mining; World Wide Web; Machine learning; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001195357,0.0009278035,0.001500393,0.001444463,0.0009128285,0.001162493,0.001634991,0.00126305,0.001631867],"category_scores_gemma":[0.002462035,0.0005451683,0.0009269498,0.00145339,0.0002415852,0.002221835,0.0009407986,0.0009387007,0.001553823],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004167715,"about_ca_system_score_gemma":0.000738399,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01188824,"about_ca_topic_score_gemma":0.02664661,"domain_scores_codex":[0.9989203,0.0001916507,0.0001117605,0.0003877798,0.0003033883,0.00008500199],"domain_scores_gemma":[0.9985909,0.0003160811,0.0001088069,0.0003164937,0.0005631593,0.0001044823],"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":[0.00121723,0.001078956,0.0405434,0.0008651547,0.001199287,0.001547202,0.0009266153,0.0649614,0.0582295,0.01049645,0.02832326,0.7906116],"study_design_scores_gemma":[0.00008360864,0.0004559286,0.01001454,0.00004845394,0.0004242718,0.001089675,0.0002871054,0.9571995,0.01251843,0.002480747,0.01524023,0.0001575571],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1349983,0.002368169,0.8447297,0.0009930113,0.0005013166,0.0005158897,0.001055628,0.006031123,0.008806864],"genre_scores_gemma":[0.7031462,0.001054958,0.2836387,0.0003997752,0.0001980445,0.0001960798,0.001217176,0.00007280578,0.01007622],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01188824,"threshold_uncertainty_score":0.02363807,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02354584567829751,"score_gpt":0.271753161437407,"score_spread":0.2482073157591095,"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."}}