Collaborative Filtering Algorithm Based on Adaptive AiNet

With the increasingly expanding of e-commerce scale, some problems, such as data sparsity and scalability problems, caused by the traditional collaborative filtering technology which is widely used in the recommender systems of e-commerce are becoming more and more prominent. At the same time, these problems decrease the recommender accuracy and influence the application effect of the recommender systems. Aiming at these problems, this paper presents a collaborative filtering algorithm based on adaptive artificial immune network. In the algorithm, the clone and mutation mechanism of the artificial immune network is utilized to get the implicit ratings to reduce the data sparsity. The algorithm uses the clone suppression and network suppression to decrease the data dimension and improve the scalability of recommender system. The experiment results indicate that the algorithm can improve the recommender accuracy.