Recently, recommender systems have attracted attention as systems that collect the enormous amount of information on the Web and suggests information to users. Recommender systems help users find the products that they want. There is a close relationship between a recommender system and the long tail because the performance of them is evaluated by not only accuracy metrics but also long tail metrics. Collaborative filtering (CF) is a typical recommender system. It is described as technology used to support the long tail. However, CF is prone to be biased towards recommending hit products. In this paper, we propose a system that recommends niche products if an item is similar to the user's preference. We will reduce the bias in top-N recommendation by using the interest in a keyword. The interest is computed from information gain, which is used to choose attributes in decision tree learning and to select features in machine learning. The results from the experiments show that the proposed system outperformed item-based CF in recommending niche products. In most existing studies focused on the long tail, niche products are recommended at the cost of accuracy. However, in our study, not only are niche products recommended but accuracy is also improved.