In this research, a revised recommendation system to generate recommended items for each user is constructed, such as “recommended for you” on the e-commerce websites. By using both the purchase and the browsing data, sparseness of matrix derived from the user's behavior history data is reduced. The main purpose is to construct a recommendation system that can recommend new items not browsed by users and appropriate items matching user preferences. As a procedure for generating recommended items, a user-item matrix and must-link constraints are first constructed from user's behavior history data. We add rows and columns to represent various item and user information to the user-item matrix. Next, semi-supervised learning is performed using the user-item matrix and the must-link constraint, and a new user-item matrix is generated. From this matrix on the basis of Pearson similarity, item similarity and user similarity are obtained. Finally, item-based collaborative filtering and user-based collaborative filtering are performed to generate recommended items. Experimental results show that the F-measure to represent the recommendation accuracy increases by generating recommended items with the proposed model using must-link constraints, user information and item information. In addition, it can be seen that the proposed model is more likely to purchase recommended items than the model of existing models.

In this paper, two portfolio selection problems including probabilistic future returns with ambiguous expected returns are proposed. Until now, many researchers have proposed models of portfolio selection problems, and there are some models considering both random conditions and ambiguous conditions, particularly using fuzzy random variables. However, the model including the random fuzzy variables has not been proposed yet. Therefore in this paper, we propose a random fuzzy portfolio selection problem. The formulated problem is transformed into a nonlinear programming problem. Finally, we construct a solution method to find a global optimal solution of the problem.