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In this paper we propose a new approach for mining association rules of classification type particularly suited for use in collaborative recommender systems. Such systems rely on information about relation- ships between different users' preferences in order to recommend items of potential interest to the target user. Despite their successful application to other domains, existing association rule mining techniques are not suitable for the recommendation domain because they mine many rules that are not relevant to a given user. Also, they require that the minimum support (also known as the significance) of the mined rules be specified in advance, often leading to too many or too few rules. In contrast, our approach adjusts the minimum support so that the number of rules obtained is within a specified range, thus avoiding excessive computation time while guaranteeing that enough rules are provided to allow good classification performance. This paper describes our approach. The results of an experimental evaluation of our approach are also described. These results show that the rules mined by our approach allow excellent recommendation performance.