PERSONALITY-AWARE PRODUCT RECOMMENDATION SYSTEM BASED ON USER INTERESTS MINING AND META-PATH DISCOVERY

Abstract

Online marketplaces often witness opinion spam in the form of reviews. People are often hired to
target specific brands for promoting or impeding them by writing highly positive or negative
reviews. This often is done collectively in groups. Although some previous studies attempted to
identify and analyze such opinion spam groups, little has been explored to spot those groups who
target a brand as a whole, instead of just products. In this article, we collected the reviews from
the Amazon product review site and manually labeled a set of 923 candidate reviewer groups.
The groups are extracted using frequent itemset mining over brand similarities such that users are
clustered together if they have mutually reviewed (products of) a lot of brands. We hypothesize
that the nature of the reviewer groups is dependent on eight features specific to a (group, brand)
pair. We develop a feature-based supervised model to classify candidate groups as extremist
entities. We run multiple classifiers for the task of classifying a group based on the reviews
written by the users of that group to determine whether the group shows signs of extremity. A
three-layer perceptron-based classifier turns out to be the best classifier. We further study
behaviors of such groups in detail to understand the dynamics of brand-level opinion fraud
better. These behaviors include consistency in ratings, review sentiment, verified purchase,
review dates, and helpful votes received on reviews. Surprisingly, we observe that there are a lot of verified reviewers showing extreme sentiment, which, on further investigation, leads to ways
to circumvent the existing mechanisms in place to prevent unofficial incentives on Amazon.