Recommender System Using Personal Values-based User Modeling from Browsing History of Customer Reviews

Abstract

This paper proposes a method for generating a user model reflecting user's personal values from user's browsing histories of customer reviews. This paper also proposes a recommendation method using the personal values-based user model. Existing recommendation methods such as collaborative filtering and content-based filtering tend to be less accurate for new users and items due to the lack of information about them. The personal values-based recommender system is expected to realize more precise recommendations for new users. As a customer review contains reviewer's evaluation of an item and its attributes, the proposed method estimates attributes on which a target user put high priority when evaluating items from customer reviews the user refers to for his/her decision making. This paper examines the effectiveness of the proposed method with user experiments.