Tag recommendation method for a cosmetics review recommender system
Yuki Matsunami, Mayumi Ueda, Shinsuke Nakajima · 2017
In recent years, although cosmetics review-sharing sites have been helpful in decision making by users, it is not easy for users to find reviews that are suitable for them because the quality of skin and taste vary among individuals. We aim to develop a recommender system for cosmetic items and reviews by analyzing cosmetics reviews. In most review sharing sites, reviewers can assign tags to their own reviews. Tags are very useful for users to understand the effects of the items and to filter reviews with specific tags. Thus, we propose a tag recommendation method for a cosmetics review using the results of the automatic scoring method proposed in our previous work. We believe that our proposed method can significantly simplify the task of assigning tags to a review text. Moreover, the results of the experimental evaluation reveal the tendency that "reviewers may select tags if their score for an aspect of a cosmetic item is sufficiently high". Based on this result, we will discuss a threshold to determine whether to recommend tags.