A study on improvement of serendipity in item-based collaborative filtering using association rule

The number of available items in online shops are increasing by the spread of the Internet recently. Though users have a wide range of choices, they need to find their favorite items from a huge amount of information. Thus, a variety of recommendation systems are currently in use. "Accuracy" is the most important index in these recommendation systems. However, not only "Accuracy" but also "Serendipity" is said to be needed in terms of user satisfaction recent years. In this paper, we introduce a recommendation method of collaborative filtering based on association analysis which is one of the data mining techniques. We aim to improve Serendipity keeping Accuracy high by using the evaluation information that are rated differently from a target user. In addition, we show that Accuracy and Serendipity can be adaptable by a parameter in the proposed method. This paper compares the proposed method with a conventional method in terms of the performance of Accuracy and Serendipity.