Abstract: A real world challenging task of an e-commerce application is to identify the needs of the active users based on user navigation patterns. Online navigation patterns are grown every day and extracting business intelligence is a challenging one. There are various personalized recommender systems have been proposed in the literature. In this paper we propose and analyse the performance of back propagation neural network model for personalized recommender system for better quality in terms of accuracy. The performance of an algorithm has been tested with different parameters with real world dataset for the performance benchmark.