I. Introduction
Information Retrieval (IR) is a fundamental task in our daily life. In popular keyword-based IR, since it is not easy to get desirable data objects by providing query keywords just once, we iteratively input our queries until we can meet some satisfiable results. Particularly, in Associative Search [5], at each step we repeatedly input a query, our query is shifted to its sibling concept [6]. As the results, we often find an interesting search result which is surprising or unexpected for us but still keeps a certain degree of relevance/similarity to our initial query. The authors consider that such an aspect of associative search is strongly desirable and useful especially for recommendation-oriented IR systems. We discuss in this paper a recommendation-oriented method for finding interesting objects for a given query. Particularly, we try to take an unexpectedness of objects for the query into account.