The massive growth of the Internet has made an enormous amount of infor- tion available to us. However, it is becoming very difficult for users to acquire an - plicable one. Therefore, some techniques such as information filtering have been - troduced to address this issue. Recommender systems filter information that is useful to a user from a large amount of information. Many e-commerce sites use rec- mender systems to filter specific information that users want out of an overload of - formation [2]. For example, Amazon. com is a good example of the success of - commender systems [1]. Over the past several years, a considerable amount of research has been conducted on recommendation systems. In general, the usefulness of the recommendation is measured based on its accuracy [3]. Although a high - commendation accuracy can indicate a user's favorite items, there is a fault in that - ly similar items will be recommended. Several studies have reported that users might not be satisfied with a recommendation even though it exhibits high recommendation accuracy [4]. For this reason, we consider that a recommendation having only accuracy is - satisfactory. The serendipity of a recommendation is an important element when c- sidering a user's long-term profits. A recommendation that brings serendipity to users would solve the problem of "user weariness" and would lead to exploitation of users' tastes. The viewpoint of the diversity of the recommendation as well as its accuracy should be required for future recommender systems.

The massive growth of the Internet has made an enormous amount of infor- tion available to us. However, it is becoming very difficult for users to acquire an - plicable one. Therefore, some techniques such as information filtering have been - troduced to address this issue. Recommender systems filter information that is useful to a user from a large amount of information. Many e-commerce sites use rec- mender systems to filter specific information that users want out of an overload of - formation [2]. For example, Amazon. com is a good example of the success of - commender systems [1]. Over the past several years, a considerable amount of research has been conducted on recommendation systems. In general, the usefulness of the recommendation is measured based on its accuracy [3]. Although a high - commendation accuracy can indicate a user's favorite items, there is a fault in that - ly similar items will be recommended. Several studies have reported that users might not be satisfied with a recommendation even though it exhibits high recommendation accuracy [4]. For this reason, we consider that a recommendation having only accuracy is - satisfactory. The serendipity of a recommendation is an important element when c- sidering a user's long-term profits. A recommendation that brings serendipity to users would solve the problem of "user weariness" and would lead to exploitation of users' tastes. The viewpoint of the diversity of the recommendation as well as its accuracy should be required for future recommender systems.