A Parallel Recommender System Using a Collaborative Filtering Algorithm with Correntropy for Social Networks

Recently, the extraction of valid information from big data has witnessed a growing interest. Nowadays, in social networks, large parts of websites collect user profiles to provide some valuable information through personalized recommendation. Among the available recommendation algorithms, collaborative filtering (CF) is one of the most popular algorithms due to its simple framework. However, in some practices, the computational time of CF may be unsatisfactory. Meanwhile, in some cases there are noises in data, i.e., some data are invalid, it also has a great impact on algorithm performance. To speed up the time it takes to make recommendation and tackle the noise issue more effectively, we developed a novel parallel recommender system based on CF with correntropy. Instead of traditional measures used in recommendation algorithms, the correntropy was employed to compute the similarity of two items or users to achieve insensitive performance to outliers. Moreover, to reduce the computational cost, we employed the Spark framework to facilitate parallel computing. The experiments on three datasets consisting data collected from actual social networks were conducted and the experimental results showed that for social networks application, the proposed system could effectively improve the computational time and achieve satisfactory performance though invalid data existed.