{"id":"W2077311880","doi":"10.1109/dsaa.2014.7058061","title":"Probabilistic Category-based Location Recommendation Utilizing Temporal Influence and Geographical Influence","year":2014,"lang":"en","type":"article","venue":"","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Component (thermodynamics); Similarity (geometry); Probabilistic logic; Location-based service; Data mining; Location; Information retrieval; Artificial intelligence; Geography; Image (mathematics)","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001842922,0.0001081555,0.0001384567,0.0001483762,0.0007136055,0.0001561573,0.0001541497,0.0001078237,0.0002107028],"category_scores_gemma":[0.001536223,0.0001054825,0.00003929858,0.0006904906,0.0005544762,0.0003464382,0.00001837915,0.0001300011,0.00002658258],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007245153,"about_ca_system_score_gemma":0.0002252138,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.02566426,"about_ca_topic_score_gemma":0.03400754,"domain_scores_codex":[0.9984459,0.0004342012,0.0002919784,0.0003423424,0.0002549078,0.0002306589],"domain_scores_gemma":[0.9987628,0.0004103398,0.0001058724,0.0002009614,0.0003713152,0.0001487155],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004102439,0.0003046849,0.6704395,0.0002142443,0.00003184547,4.203612e-7,0.004652434,0.02091377,0.000219976,0.2199282,0.0001517312,0.08310208],"study_design_scores_gemma":[0.00116567,0.0002873393,0.6694496,0.0001535004,0.0001825911,7.555062e-7,0.005789578,0.2231806,0.0002480065,0.04635251,0.05207034,0.001119456],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9665878,0.00001390849,0.02546178,0.003935204,0.00003168199,0.0003102399,0.000001272431,0.000154566,0.003503517],"genre_scores_gemma":[0.9985018,0.000007077479,0.0004691702,0.0008191905,0.00004640414,0.00004413962,0.00003398792,0.000005759349,0.00007249744],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2022668,"threshold_uncertainty_score":0.9836193,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01682618981333762,"score_gpt":0.2896015078430443,"score_spread":0.2727753180297067,"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."}}