Finding similar users based on their preferences against cosmetic item clusters

Portal sites supporting online purchases provide commercial items and reviews for them. In the case of purchasing cosmetic items, in particular, reviews have important roles in purchasing decisions, allowing purchasers to avoid becoming annoyed with unsuitable items. Thus, we are trying to develop a recommender system for cosmetic items and analyzing reviews. General recommender systems basically identify similar users based on their preferences against common items. However, owing to the huge number of cosmetic items, it is not easy to use preferences for common items because of the data sparsity problem. Therefore, we propose a method for finding similar users based on their preferences against cosmetic item clusters. Moreover, we evaluate and discuss the proposed method for finding similar users based on experimental evaluations.