{"id":"W2606751029","doi":"10.1142/s0218213017600107","title":"Characterizing Users and Tracking Their Activities in Online Classified Ads","year":2017,"lang":"en","type":"article","venue":"International Journal of Artificial Intelligence Tools","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Profiling (computer programming); Probabilistic logic; User modeling; Set (abstract data type); Class (philosophy); Generative model; Generative grammar; Data mining; Machine learning; User interface; Human–computer interaction; Information retrieval; Artificial intelligence","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":[0.001371536,0.0005374652,0.0006497682,0.002071881,0.0004904664,0.001297732,0.0007411636,0.000806152,0.0007996781],"category_scores_gemma":[0.006983208,0.000388996,0.0004556428,0.001960353,0.0003757519,0.001968482,0.0005118191,0.0009632279,0.0006376462],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005760685,"about_ca_system_score_gemma":0.0003579521,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0109838,"about_ca_topic_score_gemma":0.02115428,"domain_scores_codex":[0.9988752,0.0003920534,0.00007190045,0.0003200118,0.0002350509,0.0001057781],"domain_scores_gemma":[0.9930808,0.003692856,0.001299973,0.00082526,0.0008041334,0.0002969839],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005027294,0.0006257106,0.8154641,0.0001931016,0.0002380399,0.0002567512,0.001441601,0.04973854,0.01166507,0.003493593,0.002453778,0.113927],"study_design_scores_gemma":[0.00001617096,0.0002397158,0.3571438,0.00003537391,0.0001043365,0.0006224692,0.001000069,0.6252254,0.007136131,0.00530113,0.003098052,0.00007735381],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9377967,0.000310865,0.05666012,0.0002620856,0.00002299677,0.0001005863,0.001844058,0.0003288022,0.002673818],"genre_scores_gemma":[0.9784645,0.0001419733,0.0195534,0.00003687465,0.00002395827,0.00002901955,0.0009562112,0.00001685941,0.0007772111],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0109838,"threshold_uncertainty_score":0.02183974,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1620729212358601,"score_gpt":0.3655221996245478,"score_spread":0.2034492783886877,"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."}}