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User-generated reviews serve as crucial references in shopper'sdecision-making process. Moreover, they improve product sales and validate thereputation of the website as a whole. Thus, it becomes important to designreviews ranking methods that help shoppers make informed decisions quickly.However, reviews ranking has its unique challenges. First, there is norelevance labels for reviews. A relevant review for shopper A might not berelevant to shopper B. Second, since shoppers cannot click on reviews, we haveno ways of getting relevance feedback. Eventually, reviews ranking suffers fromthe lack of ground truth due to the variability in the standard of relevancefor different users. In this paper, we aim to address the challenges of helpingusers to find information they might be interested in from the sea of customerreviews. Using the Amazon Customer Reviews Dataset collected and organized byUCSD, we first constructed user profiles based on user's personal web trails,recent shopping history and previous reviews, incorporated user profiles intoour ranking algorithm, and assigned higher ranks to reviews that addressindividual shopper's concerns to the largest extent. Also, we leveraged userprofiles to recommend products based on reviews texts. We evaluated our modelbased on both empirical evaluations and numerical evaluations of review scores.The results from both evaluation methods reveal a significant increase in thequality of top reviews as well as user satisfaction for over 1000 products. Ourreviews based recommendation system also suggests that there's a large chanceof user viewing and liking the product we recommend. Our work shows the basicsteps of developing a ranking method that learns from a particular end-user'spreferences.

Personalized Review Ranking for Improving Shopper's Decision Making: A Term Frequency based Approach