{"id":"W2913214317","doi":"10.1109/icdmw.2018.00170","title":"TCENR: A Hybrid Neural Recommender for Location Based Social Networks","year":2018,"lang":"en","type":"article","venue":"","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University","funders":"","keywords":"Computer science; Recommender system; Collaborative filtering; Field (mathematics); Artificial neural network; Artificial intelligence; Point (geometry); Embedding; Social network (sociolinguistics); Machine learning; Social media; Data mining; Information retrieval; World Wide Web","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":[0.0009485503,0.001037937,0.001256444,0.001198749,0.0005375107,0.000658489,0.002550771,0.001487681,0.002864823],"category_scores_gemma":[0.002517893,0.0004910724,0.0008002513,0.001391198,0.0002595311,0.001500128,0.001049208,0.001310142,0.001830289],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008445696,"about_ca_system_score_gemma":0.0007634,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03615976,"about_ca_topic_score_gemma":0.08857479,"domain_scores_codex":[0.9994025,0.0001344408,0.00003540634,0.0002012643,0.0001743188,0.00005209842],"domain_scores_gemma":[0.9992036,0.0002884026,0.00005303339,0.0001600668,0.0002506991,0.00004424595],"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":[0.0004737334,0.0007276513,0.005854166,0.0003534292,0.0005425618,0.0002817298,0.0001682022,0.2408861,0.006776915,0.0080578,0.03304346,0.7028344],"study_design_scores_gemma":[0.00002233582,0.0000728468,0.0004239683,0.00001023835,0.00003404731,0.00007558589,0.00001195901,0.9948636,0.0008140564,0.001206434,0.00244915,0.00001589329],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0607864,0.003128981,0.9186219,0.001031189,0.0004698136,0.0003342312,0.002549633,0.005811413,0.007266398],"genre_scores_gemma":[0.4235575,0.00155909,0.5385587,0.0006945714,0.0003095978,0.0003658562,0.005805463,0.0002302753,0.02891907],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03615976,"threshold_uncertainty_score":0.07189858,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03576922667092084,"score_gpt":0.286561390893886,"score_spread":0.2507921642229652,"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."}}