At a recent time, digital data increases very speedily from small business to large business. In this span of internet explosion, choices are also increases and it makes the selection of products very difficult for users so it demands some recommendation system which provides good and meaningful suggestions to users to help them to purchase or select products of their own choice and get benefited. Collaborative filtering technique works very productive to provide personalized suggestions. It works based on the past given ratings, behavior and choices of users to provide recommendations. To boost its performance many other algorithms and techniques can be combined with it. This paper describes the method to boost the performance of collaborative filtering algorithm by taking multiple attributes in consideration where each attribute has some weight.