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With the rapid growth of e-commerce, there has been millions of products in a large ecommerce site where customer unable to effectively choose the products they are exposed to. To overcome the product overload problem, a variety of recommendation methods have been developed. Collaborative filtering (CF) is the most successful recommendation method. However, the CF method has two well-known limitations, sparsity and scalability, which can lead to poor recommendations. This paper proposes a new methodology, SAT-PT, to enhance the recommendation quality and the system performance of current CF-based recommender systems. SAT-PT is based on Web usage mining and product taxonomy. Several experiments shows that the proposed methodology provides higher quality recommendations and better performance than other CF methodologies.