A Personalized Approach for Recommending Useful Product Reviews Based on Information Gain

Abstract

Customer product reviews have become great influencers of purchase decision making. To assist potential customers, online stores provide various ways to sort customer reviews. Different methods have been developed to identify and recommend useful reviews to customers, primarily using feedback provided by customers about the helpfulness of reviews. Most of the methods consider the preferences of all users to determine whether reviews are helpful, and all users receive the same recommendations. In this paper, we assessed methods for generating personalized recommendations based on information gain. The information gain approach was extended to consider each individual’s preference together with votes of other users. A total of 172 respondents rated 48 reviews selected from Amazon.com using a 7-point Likert scale. The performance of the devised methods was measured by varying the ratio of training sets and number of recommendations for the data collected. The personalized methods outperformed the existing information gain method, which takes into account the votes from all users. The greatest precision was achieved by the personalized method and a method employing selective use of predictions from the personalized method combined with the existing method based on all users’ reviews. However, the personalized method, which classified helpful reviews based on each user’s threshold value, showed statistically better performance.