Personalized Opinion-Based Recommendation

E-commerce recommender systems seek out matches between customers and items in order to help customers discover more relevant and satisfying products and to increase the conversion rate of browsers to buyers. To do this, a recommender system must learn about the likes and dislikes of customers/users as well as the advantages and disadvantages (pros and cons) of products. Recently, the explosion of user-generated content, especially customer reviews, and other forms of opinionated expression, has provided a new source of user and product insights. The interests of a user can be mined from the reviews that they write and the pros and cons of products can be mined from the reviews written about them. In this paper, we build on recent work in this area to generate user and product profiles from user-generated reviews. We further describe how this information can be used in various recommendation tasks to suggest high-quality and relevant items to users based on either an explicit query or their profile. We evaluate these ideas using a large dataset of TripAdvisor reviews. The results show the benefits of combining sentiment and similarity in both query-based and user-based recommendation scenarios, and also disclose the effect of the number of reviews written by a user on recommendation performance.