{"id":"W4380632772","doi":"10.3390/fi15060213","title":"BERT4Loc: BERT for Location—POI Recommender System","year":2023,"lang":"en","type":"article","venue":"Future Internet","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; Toronto Metropolitan University","funders":"","keywords":"Computer science; Benchmark (surveying); Baseline (sea); Recommender system; Margin (machine learning); Encoder; Machine learning; Learning to rank; RSS; Artificial intelligence; Information retrieval; Data mining; Transformer; World Wide Web; Ranking (information retrieval)","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":[0.0004767828,0.001005046,0.0009020228,0.001077141,0.0005115121,0.0007240781,0.001691901,0.001100098,0.004642948],"category_scores_gemma":[0.001734216,0.0004042142,0.0005517251,0.001418133,0.0001788139,0.001415931,0.0007329321,0.0009168517,0.003875205],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007782911,"about_ca_system_score_gemma":0.001024805,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03274101,"about_ca_topic_score_gemma":0.0732788,"domain_scores_codex":[0.9997155,0.00005675979,0.00001655024,0.00007329882,0.0001059714,0.00003195964],"domain_scores_gemma":[0.9995334,0.0001036284,0.0000235566,0.0001313831,0.0001747608,0.00003319635],"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":[0.0008896034,0.0005210388,0.0123715,0.0006212518,0.0003172221,0.0004943556,0.000235683,0.1719276,0.01564117,0.009675761,0.1308194,0.6564854],"study_design_scores_gemma":[0.00004190786,0.0001172902,0.001435294,0.0000169103,0.00004930278,0.0002319751,0.00002855465,0.9740768,0.00356505,0.002150381,0.0182414,0.00004505689],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06357846,0.003467312,0.8442485,0.001314975,0.0004563554,0.0005737839,0.01137101,0.0581877,0.01680193],"genre_scores_gemma":[0.5280963,0.001849684,0.416274,0.0004922161,0.0001789768,0.0003672457,0.02132165,0.0006649948,0.03075492],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03274101,"threshold_uncertainty_score":0.06510085,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02113360149218444,"score_gpt":0.2627728002228634,"score_spread":0.2416391987306789,"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."}}