Abstract : This paper focuses on Taobao cheater detection. At present the phenomenon of fake trading is widespread in Taobao, which makes it difficult for consumers to distinguish between true and fake product reviews. To solve this problem, we collect a total number of 50,285 historical review data from 100 cheaters and 100 real buyers to create a dataset. By using these data, we extract 8 features from three dimensions that are reviewer, commodity, and review. Then we use the SVM algorithm to construct the classification model and choose the RFB kernel function, which has a better performance to identify the cheater. The precision of the final classification model we built to identify the cheater reaches up to 89%. The experimental result shows that extracting features from the historical review data can recognize the cheaters effectively. It can be applied to the recognition of the cheaters in Taobao.
Hongxiu Li; Matti Mäntymäki; Xianfeng Zhang. 13th Conference on e-Business, e-Services and e-Society (I3E), Nov 2014, Sanya, China. Springer, IFIP Advances in Information and Communication Technology, AICT-445, pp.338-345, 2014, Digital Services and Information Intelligence. 〈10.1007/978-3-662-45526-5_31〉
https://hal.inria.fr/hal-01342164
Contributeur : Hal Ifip
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Soumis le : mardi 5 juillet 2016 - 14:48:09
Dernière modification le : mardi 5 juillet 2016 - 14:59:24
Baohua Dong, Qihua Liu, Yue Fu, Liyi Zhang. A Research of Taobao Cheater Detection. Hongxiu Li; Matti Mäntymäki; Xianfeng Zhang. 13th Conference on e-Business, e-Services and e-Society (I3E), Nov 2014, Sanya, China. Springer, IFIP Advances in Information and Communication Technology, AICT-445, pp.338-345, 2014, Digital Services and Information Intelligence. 〈10.1007/978-3-662-45526-5_31〉. 〈hal-01342164〉
