Recommending Items with Conditions Enhancing User Experiences Based on Sentiment Analysis of Reviews

In this paper we propose a new method of recommending not only items of interest to the user but also the conditions enhancing user experiences with those items, such as recommending to go to a restaurant for seafood. This method is based on the sentiment analysis of user reviews, predicts sentiments that the user might express about the aspects determined in an application, and identifies the most valuable aspects of user’s potential experience with the item. Furthermore, our method recommends the items together with those most important aspects over which the user has control and can potentially select them, such as the time to go to a restaurant, e.g. lunch vs. dinner, or what to have there, such as seafood. We tested our method on three applications (restaurants, hotels and beauty&spas) and experimentally showed that those users who followed our recommendations of items with their corresponding conditions had better experiences, as defined by the overall rating, than others.