Product recommendation is very important in business to customer (B2C) e-commerce. Automated Collaborative Filtering (ACF) is an important approach for product recommendation. However, a major drawback with this approach is that it can't avoid the "sequence recognition problem", explained in this paper. Here we present a system that addresses the sequence recognition problem by recording and utilizing the users' purchase patterns and ratings. The proposed system is a fruitful combination of ACF and Case-Based Reasoning Plan Recognition (CBRPR) methods. The evaluation studies prove that the hybrid system provides better performance when compared to ACF and CBRPR methods used individually.
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