{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004380232,0.0001704384,0.0002141366,0.0001674488,0.00007189361,0.0002001772,0.0009461708,0.0001247616,0.00001135715],"category_scores_gemma":[0.00001448205,0.0001430096,0.000106236,0.0004578061,0.00001004572,0.0002700432,0.0002206255,0.0001149416,0.000229054],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009880022,"about_ca_system_score_gemma":0.00003175362,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001088316,"about_ca_topic_score_gemma":0.00004998573,"domain_scores_codex":[0.998719,0.0000575125,0.0003271142,0.0004179553,0.0001671998,0.0003112699],"domain_scores_gemma":[0.9989792,0.0001017719,0.0001136994,0.0005976326,0.0001274342,0.00008025886],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000004255206,0.00001561987,0.0001330917,0.0001374238,0.00004021737,0.000006242582,0.0009822306,0.000002452477,0.00002482436,0.16202,0.8117048,0.0249289],"study_design_scores_gemma":[0.0002442615,0.00009991764,0.0003466626,0.0001360886,0.000005643617,0.00004691159,0.0003368185,0.02981402,0.001355712,0.001427563,0.96595,0.0002363803],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0005313738,0.0001909189,0.9696168,0.01246422,0.006031565,0.0006658771,0.00001078979,0.00237445,0.008114018],"genre_scores_gemma":[0.950516,0.00002034324,0.03557975,0.001203022,0.00202178,0.0006479797,0.00006598863,0.00004641279,0.00989874],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9499846,"threshold_uncertainty_score":0.5831764,"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."}}