{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007456454,0.0007409006,0.0006353726,0.004197037,0.00101163,0.002865233,0.0007964624,0.00133673,0.001844861],"category_scores_gemma":[0.07583565,0.0004895712,0.0004622531,0.001794666,0.0004923421,0.002787923,0.00075299,0.0009170587,0.001375757],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007658376,"about_ca_system_score_gemma":0.001152985,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003253844,"about_ca_topic_score_gemma":0.008280749,"domain_scores_codex":[0.9939889,0.002106152,0.0006701814,0.00107112,0.001762211,0.0004014052],"domain_scores_gemma":[0.8452938,0.07274114,0.04543535,0.003284122,0.02576157,0.007483969],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003730094,0.0002694787,0.9565096,0.0002242775,0.0001082448,0.000330662,0.0008202086,0.001503763,0.00223484,0.0003762121,0.002719223,0.03453058],"study_design_scores_gemma":[0.00004114044,0.000642703,0.9422721,0.00009576145,0.0002010163,0.001069092,0.001321843,0.04424616,0.004290935,0.0008799171,0.004833684,0.0001056244],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9902463,0.001838399,0.004548497,0.0003977214,0.00007115436,0.0001540908,0.0004177641,0.0001084503,0.002217649],"genre_scores_gemma":[0.9932697,0.0004714009,0.003817499,0.00005254969,0.0001015396,0.00005683462,0.0004062215,0.00002776727,0.001796525],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007456454,"threshold_uncertainty_score":0.03943396,"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."}}