{"id":"W2922891144","doi":"10.1155/2019/2926749","title":"A Joint Deep Recommendation Framework for Location‐Based Social Networks","year":2019,"lang":"en","type":"article","venue":"Complexity","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Recommender system; Popularity; Deep learning; Artificial intelligence; Convolutional neural network; Machine learning; Perceptron; Social network (sociolinguistics); Social media; Artificial neural network; Data science; World Wide Web","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":[0.0006360727,0.0009308974,0.0009045342,0.0007281004,0.0004099411,0.0008709116,0.001804161,0.001240445,0.003293976],"category_scores_gemma":[0.001627208,0.0006097136,0.0009441985,0.0009704623,0.0003433308,0.001521984,0.0008670614,0.001707011,0.001322256],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001218972,"about_ca_system_score_gemma":0.001132966,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05243466,"about_ca_topic_score_gemma":0.1005988,"domain_scores_codex":[0.9996081,0.00009555095,0.00002144514,0.000127564,0.00008871742,0.00005855425],"domain_scores_gemma":[0.9996375,0.0001225891,0.00003875211,0.00005394398,0.0001144001,0.00003282513],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001722162,0.0001893298,0.002811815,0.0001882848,0.0002996929,0.0002382161,0.000157723,0.690008,0.002840759,0.04102295,0.0170646,0.2450063],"study_design_scores_gemma":[0.000004018524,0.00001250958,0.0001129186,0.000004972755,0.000009163557,0.00001251658,0.000004695039,0.9948548,0.0001451827,0.003862534,0.0009725667,0.000004052178],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01895694,0.001356739,0.9714361,0.0009480692,0.0001316011,0.00006464434,0.001331941,0.001630627,0.004143333],"genre_scores_gemma":[0.6579573,0.001883936,0.3032628,0.0006556399,0.0002438467,0.000266692,0.004071089,0.0001885865,0.03147019],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05243466,"threshold_uncertainty_score":0.1042589,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1030737706845544,"score_gpt":0.316591711333471,"score_spread":0.2135179406489167,"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."}}