Recommendation systems on the Internet have become more necessary due to enormous amounts of information that keep increasing. Existing recommendation systems, such as Content-Based Filtering (CBF) and Collaborative Filtering (CF), have a trade off: recommended items cannot reflect users' preferences and offer valid unexpected elements at the same time.Our goal is to resolve this trade-off problem. We propose a recommendation system that uses four-term analogy, which is a way of thinking. We prove the proposed system's effectiveness by comparing it with existing systems.

Recently, though there are a lot of techniques to rank information there are few studies on how to rank users and thus help to form online communities. We propose the use of a system to recommend the user by analyzing his or her interests, and using Conceptual Fuzzy Sets to expand a query. We show the effectiveness of using Conceptual Fuzzy Sets for recommending users. This can be applied to forming communities.