{"id":"W2796213239","doi":"10.1109/tkde.2018.2821671","title":"Characterizing and Predicting Early Reviewers for Effective Product Marketing on E-Commerce Websites","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Knowledge and Data Engineering","topic":"Digital Marketing and Social Media","field":"Social Sciences","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"National Key Research and Development Program of China; Innovate UK; Natural Science Foundation of Beijing Municipality; Renmin University of China; National Natural Science Foundation of China","keywords":"Helpfulness; Popularity; Product (mathematics); Computer science; New product development; Information retrieval; Artificial intelligence; World Wide Web; Psychology; Marketing; Mathematics; Business; Social psychology","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":[0.001788232,0.0001471075,0.0001884078,0.00009598926,0.0006355236,0.0001433864,0.000161898,0.00006180073,0.000006823417],"category_scores_gemma":[0.0008170356,0.0001502987,0.00003279868,0.0001870432,0.0001124002,0.0004266698,0.000007008889,0.0001713777,0.00001083229],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003838036,"about_ca_system_score_gemma":0.00002718995,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003749277,"about_ca_topic_score_gemma":0.00009723568,"domain_scores_codex":[0.9989306,0.0001158687,0.0001597029,0.0003912893,0.000115217,0.0002872844],"domain_scores_gemma":[0.9980558,0.001474484,0.00004235973,0.0002244674,0.00007875067,0.0001240958],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001925439,0.0001288871,0.0008824397,0.0005160551,0.0001124901,0.000001242459,0.01571827,0.000008987198,0.001790157,0.000159189,0.0005790615,0.9799107],"study_design_scores_gemma":[0.004197708,0.002368133,0.07435237,0.01346957,0.0008463509,0.00001597648,0.009126152,0.03058782,0.01219676,0.0001231308,0.8490614,0.003654601],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9484434,0.001410692,0.02837423,0.000584228,0.003624384,0.001935438,0.0002868291,0.0005348343,0.01480594],"genre_scores_gemma":[0.9977687,0.0003906885,0.0005486156,0.00003483989,0.000816381,0.00006866836,0.000008362794,0.00002514576,0.0003386252],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9762561,"threshold_uncertainty_score":0.6129003,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02389185359157603,"score_gpt":0.3001767249798764,"score_spread":0.2762848713883004,"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."}}