{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00110555,0.0007683904,0.001549283,0.005091812,0.0008261494,0.001025758,0.002631185,0.001323928,0.00135677],"category_scores_gemma":[0.006705117,0.0004718385,0.001433619,0.005925143,0.0004079117,0.001875501,0.0008759175,0.0007995585,0.0009785827],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009119401,"about_ca_system_score_gemma":0.001323252,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04535346,"about_ca_topic_score_gemma":0.07528337,"domain_scores_codex":[0.9982408,0.0002898677,0.0001449454,0.0004743624,0.0006945723,0.0001553661],"domain_scores_gemma":[0.9960839,0.001693241,0.0003246664,0.000453269,0.001284241,0.0001608008],"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":[0.0005868273,0.0005629849,0.07305928,0.0004994757,0.0006851879,0.0004710545,0.0004377128,0.1848657,0.008047281,0.00696764,0.01765157,0.7061653],"study_design_scores_gemma":[0.00003430129,0.00009532036,0.009381401,0.00002959625,0.0001075732,0.0004195048,0.0000840994,0.9817132,0.001275624,0.003595643,0.003212142,0.00005162589],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1730552,0.003581893,0.8087325,0.0007666718,0.0002301119,0.0004236591,0.002415158,0.003070878,0.007723976],"genre_scores_gemma":[0.8052391,0.0009819679,0.1842873,0.0002283716,0.0002810312,0.00021698,0.004154632,0.0001078681,0.004502669],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04535346,"threshold_uncertainty_score":0.09017897,"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."}}