Abstract
In e-commerce websites and related micro-blogs, users supply online reviews expressing their preferences regarding various items. Such reviews are typically in the textual comments form, and account for a valuable information source about user interests. Recently, several works have used review texts and their related rich information like review words, review topics and review sentiments, for improving the rating-based collaborative filtering recommender systems. These works vary from one another on how they exploit the review texts for deriving user interests. This paper provides a detailed survey of recent works that integrate review texts and also discusses how these review texts are exploited for addressing some main issues of standard collaborative filtering algorithms.
Highlights
Nowadays, e-commerce websites have been flourishing quickly and permitting millions of items for selling [1]
To evaluate the performance of Collaborative Filtering (CF)-based recommender system (RS) many evaluation measures have been used by research communities in RS [37]
Due to the occurrence of modern text mining techniques, much effort has been devoted to incorporating review texts into the recommending task
Summary
E-commerce websites have been flourishing quickly and permitting millions of items for selling [1]. Another limitation is that CF approaches do not catch the reason for ratings of the user, and cannot precisely catch the preference of a target user [8] To deal with these problems, several content-based methods have been developed to represent users and items by various kinds of data, including tags [9], items’ descriptions [10], and social factors [11]. This paper focuses on user review texts and surveys the recent studies that integrate the rich information contained in reviews in order to mitigate the main issues of the standard rating-based systems like sparsity and prediction accuracy problems.
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