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Prediction systems apply knowledge discovery techniques to the problem of making personalized product recommendations. The tremendous growth of customers and products in recent years, poses some key challenges for prediction systems, as these are producing high quality recommendations per seconds for millions of customers and products. New recommender system technologies are needed that can quickly produce quality recommendations, even for very large-scale problems. This paper presents a new and efficient approach that works using Bayesian belief networks (BBN) and that calculate the probabilities of interdependent events by giving each parent event a weighting (Expert systems). To get best result for the sales data prediction, different weights has been applied on the proposed algorithm. Finally we got results for a given product, using our proposed algorithm.