Konstantin Bauman

Assistant Professor


Recommending Items with the Most Valuable Aspects Based on User Reviews

In this study, 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 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 about 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.

Contact Information

Department of Management Information Systems
Fox School of Business,
Temple University

206B Speakman Hall,
1810 North 13th Street
Philadelphia, PA 19122-6083

Phone: (215)204-3750
Email: kbauman@temple.edu

Office Hours

Tuesday 12:00pm - 1:50pm
Thursday 12:00am - 1:50pm
Other times by appointment

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