An improved neighbor selection algorithm in collaborative filtering

Nowadays, customers spend much time and effort in finding the best suitable goods since more and more information is placed online. To save their time and effort in searching the goods they want, a customized recommender system is required. In this paper we present an improved neighbor selection algorithm that exploits a graph approach. The graph approach allows us to exploit the transitivity of similarities. The algorithm searches more efficiently for set of influential customers with respect to a given customer. We compare the proposed recommendation algorithm with other neighbor selection methods. The experimental results show that the proposed algorithm outperforms other methods.