Using Opinion Mining in Context-Aware Recommender Systems: A Systematic Review
AbstractRecommender 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. View Full-Text
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Sundermann, C.V.; Domingues, M.A.; Sinoara, R.A.; Marcacini, R.M.; Rezende, S.O. Using Opinion Mining in Context-Aware Recommender Systems: A Systematic Review. Information 2019, 10, 42.
Sundermann CV, Domingues MA, Sinoara RA, Marcacini RM, Rezende SO. Using Opinion Mining in Context-Aware Recommender Systems: A Systematic Review. Information. 2019; 10(2):42.Chicago/Turabian Style
Sundermann, Camila V.; Domingues, Marcos A.; Sinoara, Roberta A.; Marcacini, Ricardo M.; Rezende, Solange O. 2019. "Using Opinion Mining in Context-Aware Recommender Systems: A Systematic Review." Information 10, no. 2: 42.
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