—Surging smartphone use and pervasive O2O services mean customers can post their reviews about restaurants and shops online. However, many merchants may hire some people to post positive but fraud reviews in order to attract more customers. Therefore, a model need to be built to detect spam reviews. In this paper, firstly, we build a detection model using traditional batch processing which view the detection as a binary classification problem. Next, since many reviews are coming sequentially, batch processing is not efficient and useful. We will use another incremental algorithm—Hoeffding Option Tree to update the model without processing the past data repeatedly. We find that the incremental method can drastically improve the speed and the accuracy is also satisfying.
—Incremental classification, fraud review, hoeffding option tree.
Maoan Wang, Yifan Wu, Guoshi Wu are with Beijing University of Posts and Telecommunications, Beijing, China (e-mail: firstname.lastname@example.org, email@example.com, firstname.lastname@example.org).
Jun Sun is with Information Center, China Waterborne Transport Research Institute, MOT, Beijing, China (e-mail: email@example.com).
Cite: Maoan Wang, Jun Sun, Yifan Wu, and Guoshi Wu, "An Algorithm Model For Incremental Dectection of Spam Reviews," International Journal of Modeling and Optimization vol. 6, no. 1, pp. 45-48, 2016.