Deviation-based spam-filtering method via stochastic approach

Abstract

In the presence of a huge number of possible purchase choices, ranks or ratings of items by others often play very important roles for a buyer to make a final purchase decision. Perfectly objective rating is an impossible task to achieve, and we often use an average rating built on how previous buyers estimated the quality of the product. The problem of using a simple average rating is that it can easily be polluted by careless users whose evaluation of products cannot be trusted, and by malicious spammers who try to bias the rating result on purpose. In this letter we suggest how trustworthiness of individual users can be systematically and quantitatively reflected to build a more reliable rating system. We compute the suitably defined reliability of each user based on the user's rating pattern for all products she evaluated. We call our proposed method as the deviation-based ranking, since the statistical significance of each user's rating pattern with respect to the average rating pattern is the key ingredient. We find that our deviation-based ranking method outperforms existing methods in filtering out careless random evaluators as well as malicious spammers.

Machine Learning Techniques for Spam Detection in Email and IoT Platforms: Analysis and Research Challenges

“…After this stage, rule-based filters are implemented, recognizing the sender using the subject line and user-defined parameters. Eventually, allowance and task filters are used by implementing a method that allows the account holder to send the mail [26].…”

User credibility evaluation for reputation measurement of online service

“…The iterative refinement process continuously updates the user credibility scores and group dimensions until convergence. Furthermore, based on user rating distributions, Lee D. et al (2018) unveiled a novel perspective by giving a spam filtration method called deviation ranking (DR), which calculates user reputation values based on the assumption that user rating behavior follows a normal distribution. The consensus is that users with higher rankings typically exhibit fewer biased ratings.…”

“…Users may deviate from expressing their genuine views because of conflicts of interest or a desire to shape others’ perceptions. Several studies (Zhou et al , 2011; Gao J. et al , 2015; Gao J. et al , 2017; Lee D. et al , 2018; Huang et al , 2021) have recognized the importance of evaluating the authenticity of user ratings. The proposed methods use different techniques to combine user credibility to measure service reputation.…”

Improving the Performance of Reputation Evaluation by Combining a Network Structure With Nonlinear Recovery

“…Gao et al proposed groupbased ranking (GR) and iterative group-based ranking (IGR) algorithms, which group evaluators according to their ratings [29,30] and measure the evaluators' reputation according to the sizes of the corresponding groups [31]. Other scholars employed the deviation-based ranking (DR) method to model evaluators' reputation [32], and Sun et al combined this method with GR to construct the iterative optimization ranking (IOR) [33]. In addition, there are some other methods, such as the Bayesianbased method [7,34] and others [35].…”