Context-Aware Recommendation Based On Review Mining

Recommender systems are important building blocks in many of today’s e-commerce applications including targeted advertising, personalized marketing and information retrieval. In recent years, the importance of contextual information has motivated many researchers to focus on designing systems that produce personalized recommendations in accordance with the available contextual information of users. Compared to the traditional systems that mainly utilize users’ preference history, context-aware recommender systems provide more relevant results to users. We introduce a contextaware recommender system that obtains contextual information by mining user reviews and combining them with user rating history to compute a utility function over a set of items. An item utility is a measure that shows how much it is preferred according to user’s current context. In our system, the context inference is modeled as a supervised topicmodeling problem in which a set of categories for a contextual attribute constitute the topic set. As an example application, we used our method to mine hidden contextual data from customers’ reviews of hotels and use it to produce context-aware recommendations. Our evaluations suggest that our system can help produce better recommendations in comparison to a standard kNN recommender system.