Improved Twin Support Vector Machine and Its Application on Personalized Recommendation

Abstract

With the rapid development of electronic commerce (E-commerce), information overload has become an issue in people's daily lives. Personalized products and services have thus drawn wide attentions, and personalized recommendation techniques provide effective tools to capture the user's interests and find out most relevant information to the user. In this paper, a new personalized recommendation algorithm based on improved twin support vector machine (TWSVM) is proposed. Firstly, we introduce the smoothing techniques to TWSVM (STWSVM). Then, the primal quadratic programming problems of TWSVM are transformed to be smooth unconstrained minimization problems. Followed by this, we apply the sample dynamic update strategy and STWSVM on the personalized recommendation, and compare the proposed method with conventional methods including the correlation-based, back propagation (BP)-based, and SVM-based recommended methods. The experimental results show that the proposed method has superior performance than the other methods.