Abstract
Despite the existence of dierent methods, including data mining techniques, available to be used in recommender systems, such systems still contain numerous limitations. They are in a constant need for personalization in order to make effective suggestions and to provide valuable information of items available. A way to reach such personalization is by means of an alternative data mining technique called classification based on association, which uses association rules in a prediction perspective. In this work we propose a hybrid methodology for recommender systems, which uses collaborative altering and content-based approaches in a joint method taking advantage from the strengths of both approaches. Moreover, we also employ fuzzy logic to enhance recommendations quality and eectiveness. In order to analyze the behavior of the techniques used in our methodology, we accomplished a case study using real data gathered from two recommender systems. Results revealed that such techniques can be applied eectively in recommender systems, minimizing the eects of typical drawbacks they present.