In this paper, we propose a new framework based on data mining algorithms for building a Web-page recommender system. A recommender system is an intermediary program (or an agent) with a user interface that automatically and intelligently generates a list of information which suits an individual's needs. Two information filtering methods for providing the recommended information are considered: (1) by analyzing the information content, i.e., content-based filtering, and (2) by referencing other user access behaviors, i.e., collaborative filtering. By using the data mining algorithms, the information filtering processes can be performed prior to the actual recommending process. As a result, the system response time could be improved and thus, making the framework scalable.