{"id":"W2419390560","doi":"10.1007/s41060-016-0011-4","title":"Similarity-based probabilistic category-based location recommendation utilizing temporal and geographical influence","year":2016,"lang":"en","type":"article","venue":"International Journal of Data Science and Analytics","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Dynamic time warping; Computer science; Similarity (geometry); Probabilistic logic; Matching (statistics); Component (thermodynamics); Data mining; Information retrieval; Pattern recognition (psychology); Artificial intelligence; Mathematics; Statistics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009632082,0.0007825274,0.001873204,0.005153777,0.0008951025,0.001124336,0.002988196,0.00143153,0.001907162],"category_scores_gemma":[0.005442224,0.000452503,0.001505499,0.005770267,0.0004449339,0.002095133,0.001331401,0.0007682472,0.001234937],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000733826,"about_ca_system_score_gemma":0.001262603,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02490354,"about_ca_topic_score_gemma":0.04608628,"domain_scores_codex":[0.9981208,0.0002485506,0.0001688221,0.0005428339,0.0007212938,0.0001976006],"domain_scores_gemma":[0.9967088,0.001354315,0.0002643523,0.0003307488,0.001163307,0.0001784991],"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":[0.001855085,0.00163978,0.1134862,0.0007022208,0.001282366,0.0007630478,0.0006075558,0.1872548,0.0154549,0.007640166,0.01876852,0.6505452],"study_design_scores_gemma":[0.00003203307,0.0001283579,0.007528071,0.00002403896,0.0001343964,0.00024521,0.0001043395,0.9867108,0.001290012,0.002765462,0.0009955497,0.00004171331],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3256679,0.002813849,0.6596156,0.0007032041,0.0003705586,0.0003719812,0.003038028,0.001699462,0.005719419],"genre_scores_gemma":[0.9237347,0.0004803067,0.06926335,0.0001364841,0.0002111974,0.0001403766,0.002889186,0.00004700275,0.003097424],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02490354,"threshold_uncertainty_score":0.04951715,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06415753661921661,"score_gpt":0.369871203293933,"score_spread":0.3057136666747163,"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."}}