A novel approach for book recommendation systems

Abstract

Recommendation systems are widely used in ecommerce applications. A recommendation system intends to recommend the items or products to a particular user, based on user's interests, other user's preferences, and their ratings. To provide a better recommendation system, it is necessary to generate associations among products. Since e-commerce and social networking sites generates massive data, traditional data mining approaches perform poorly. Also, the pattern mining algorithm such as the traditional Apriori suffers from high latency in scanning the large database for generating association rules. In this paper we propose a novel pattern mining algorithm called as Frequent Pattern Intersect algorithm (FPIntersect algorithm), which overcomes the drawback of Apriori. The proposed method is validated through simulations, and the results are promising.