Integrating User Reviews and Ratings for Enhanced Personalized Searching

Integrating User Reviews and Ratings for Enhanced Personalized Searching

Shuyue Hu (School of Software Engineering, South China University of Technology, Guangzhou, China), Yi Cai (School of Software Engineering, South China University of Technology, Guangzhou, China), Ho-fung Leung (Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong), Dongping Huang (School of Software Engineering, South China University of Technology, Guangzhou, China) and Yang Yang (School of Software Engineering, South China University of Technology, Guangzhou, China)
Copyright: © 2017 |Pages: 16
DOI: 10.4018/IJDET.2017040106
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Abstract

With the development of e-commerce, websites such as Amazon and eBay have become very popular. Users post reviews of products and rate the helpfulness of reviews on these websites. Reviews written by a user and reviews rated by a user reflect the user's interests and disinterest. Thus, they are very useful for user profiling. In this study, the authors explore users' reviews and ratings of reviews for personalized searching and propose a review-based user profiling method. To satisfy a user's basic information needs, expressed in the form of a query, they also propose a priority-based result ranking strategy. For evaluation, they conduct experiments on a real-life data set. The experimental results show that their method can significantly improve retrieval quality.
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1. Introduction

With the increase in resources available on the Web, resource searching has become more important and challenging. In a traditional search system, a user first issues a query containing some keywords. Then, the search engine retrieves resources according to the match between the keywords in the query and the descriptions of the available resources. Although different users may have different personal interests and expect different resources to be identified, a traditional search system always returns the same results for users issuing the same query. Therefore, to retrieve resources that not only satisfy a user’s basic information needs but also satisfy the user’s interests, it is necessary to conduct a personalized search based on user profiles.

Many studies (Ghorab, Zhou, O’Connor, & Wade, 2013; Haiduc et al., 2013) have been conducted on using data that reflect a user’s interests to construct user profiles for personalized searching. These data include a user’s stated interests (Ma, Pant, & Sheng, 2007), browsing history (Matthijs & Radlinski, 2011), social annotations (Xu, Bao, Fei, Su, & Yu, 2008), and microblogging behavior (Younus, O’Riordan, & Pasi, 2014). However, few studies have investigated leveraging users’ reviews and ratings of reviews on e-commerce websites for personalized searching.

In recent years, websites such as Amazon1, Epinions2, and Ciao UK3 have become extremely popular. These websites allow users to post reviews of products and rate the helpfulness of other users’ reviews. For example, a user can choose whether a review is helpful or not on Amazon, and can assign a helpfulness rating (ranging from 1 to 5) to a review on Epinions. These data (reviews and ratings of reviews) provide rich information for identifying personal interests or concerns. Each review includes several features describing a product. For example, there may be features such as battery life, flash, and shutter speeds in a review of a camera. Intuitively, a user may be interested in (or be concerned about) a product feature mentioned in a review they rate as useful. It is also intuitive for a user to be disinterested in (or unconcerned about) a product feature that appears in a review they rate as useless. For instance, if a user writes a review on the Galaxy S5 stating that “The processor of the phone is very fast,” then a phone’s processor may be the feature with which the user is most concerned. If another user considers this to be a useful review (i.e., rates it with a high score), then a phone’s processor may also be the feature with which that user is most concerned. By contrast, if a user considers it to be a useless review (i.e., rates it with a low score), a phone’s processor may be a feature with which this user is unconcerned.

Based on the above discussion, we believe that users’ reviews and ratings of reviews on e-commerce websites reflect a user’s concerns. Therefore, they are very useful for user profiling, and can improve the precision of user profiles. Moghaddam, Jamali, & Ester (2012) used users’ reviews to achieve personalized review recommendations based on tensor factorization. However, existing studies do not make use of them for personalized searching.

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