Abstract
Recommender systems help users by recommending items, such as products and services, that can be of interest to these users. Context-aware recommender systems have been widely investigated in both academia and industry because they can make recommendations based on a user’s current context (e.g., location and time). Moreover, the advent of Web 2.0 and the growing popularity of social and e-commerce media sites have encouraged users to naturally write texts describing their assessment of items. There are increasing efforts to incorporate the rich information embedded in user’s reviews/texts into the recommender systems. Given the importance of this type of texts and their usage along with opinion mining and contextual information extraction techniques for recommender systems, we present a systematic review on the recommender systems that explore both contextual information and opinion mining. This systematic review followed a well-defined protocol. Its results were based on 17 papers, selected among 195 papers identified in four digital libraries. The results of this review give a general summary of the current research on this subject and point out some areas that may be improved in future primary works.
Highlights
Nowadays, with the growth of the digital universe, e-commerce, and social networks, a great diversity of information, products, and services is available on the Web
The review reported in this paper was conducted with the general goals of: (i) identifying the research on context-aware recommender systems that use information extracted by opinion mining; (ii) and mapping how this research is combining these two technologies
Based on the results presented we found that the number of research works that effectively uses opinion mining in context-aware recommender systems is still low (17 primary studies)
Summary
With the growth of the digital universe, e-commerce, and social networks, a great diversity of information, products, and services is available on the Web. A recommender system is an information filtering technology that can be used to predict ratings for items (products, services, movies, among others), and/or generate a custom item ranking which may be of interest to the target user [1]. According to Adomavicius and Tuzhilin [10], recommender systems have become an independent area in the mid-1990s and since these systems have been increasingly used in a number of application fields Such systems aim to help users by indicating which items they might be interested in, making the user’s search easier. Non-personalized systems do not consider the user preferences to make the recommendations They are based on the most popular items, on the best-evaluated items, and even on newly released items to generate a list of recommendations [21]. Their advantage is that they are simple and can be implemented
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