An Empirical Study of User Awareness by Visualizing User Profile for Information Recommendation

Abstract

Many recommender systems construct user profiles by conducting machine learning on users' preference data for showing users their favorite items. Traditionally, researchers of recommender systems have pursued the accuracy of the recommendation. However, recent research trends are changing to the value improvement on the overall services including recommendation process. We especially focused on users' notices while using recommender systems. If the acquired user profile is shown to the user, he might conceive some notices on his preference because user profiles are constructed implicitly based on the users' usual activities such as browsing and shopping. In this study, we investigate the relationship between the visualization of user profiles and their notices on their preferences.