KDD Papers

Recommending Items with the Most Valuable Aspects Based on User Reviews

Konstantin Bauman (Stern School of Business, New York University);Bing Liu (University of Illinois at Chicago);Alexander Tuzhilin (Stern School of Business, New York University)


In this paper, we propose a recommendation technique that not only can recommend items of interest to the user as traditional recommendation systems do but also specific aspects of consumption of the items to further enhance the user experience with those items. For example, it can recommend the user to go to a specific restaurant (item) and also order some specific foods there, e.g., seafood (an aspect of consumption). Our method is called {\em Sentiment Utility Logistic Model} (SULM). As its name suggests, SULM uses sentiment analysis of user reviews. It first predicts the sentiment that the user may have on the item based on what he/she might express about the aspects of the item and then identifies the most valuable aspects of the user’s potential experience with that item. Furthermore, the method can recommend 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, and what to order there, e.g., seafood. We tested the proposed method on three applications (restaurant, hotel, and beauty\&spa) and experimentally showed that those users who followed our recommendations of the most valuable aspects while consuming the items, had better experiences, as defined by the overall rating.