An Adaptive Recommendation Method Based on Small-World Implicit Trust Network

Collaborative filtering (CF) is widely used in e-commerce recommender systems, which helps the online users to identify the right products to purchase. However, CF-based recommender systems suffer poor quality of recommendation due to the sparsity issue. To address this problem, in this paper we propose an adaptive recommendation method based on small-world implicit trust network. We first present a method to construct the small-world implicit trust network based on user clustering and implicit trust among users. Then we develop an adaptive recommendation algorithm by taking into account the topology of the constructed trust network, which generates recommendations using different strategies. To demonstrate the effectiveness of the proposed method, we conduct experiments on the MovieLens dataset and compare our method with others. Experimental results show that the proposed method can significantly improve the quality of recommendation.