Improved Twin Support Vector Machine and Its Application on Personalized Recommendation

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.

[1]  Yoon Ho Cho,et al.  Collaborative filtering with ordinal scale-based implicit ratings for mobile music recommendations , 2010, Inf. Sci..

[2]  李涛,et al.  Personalized News Recommendation：A Review and an Experimental Investigation , 2011 .

[3]  Madan Gopal,et al.  Knowledge based Least Squares Twin support vector machines , 2010, Inf. Sci..

[4]  Xiaojun Chen,et al.  Global and superlinear convergence of the smoothing Newton method and its application to general box constrained variational inequalities , 1998, Math. Comput..

[5]  Reshma Khemchandani,et al.  Twin Support Vector Machines for Pattern Classification , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Michael J. Pazzani,et al.  Learning and Revising User Profiles: The Identification of Interesting Web Sites , 1997, Machine Learning.

[7]  John Riedl,et al.  GroupLens: an open architecture for collaborative filtering of netnews , 1994, CSCW '94.

[8]  Xinjun Peng,et al.  TSVR: An efficient Twin Support Vector Machine for regression , 2010, Neural Networks.

[9]  Yuan-Hai Shao,et al.  Probabilistic outputs for twin support vector machines , 2012, Knowl. Based Syst..

[10]  Yuh-Jye Lee,et al.  SSVM: A Smooth Support Vector Machine for Classification , 2001, Comput. Optim. Appl..

[11]  Dean P. Foster,et al.  Clustering Methods for Collaborative Filtering , 1998, AAAI 1998.

[12]  Tao Li,et al.  Product recommendation with temporal dynamics , 2012, Expert Syst. Appl..

[13]  John Riedl,et al.  An Algorithmic Framework for Performing Collaborative Filtering , 1999, SIGIR Forum.

[14]  David Heckerman,et al.  Empirical Analysis of Predictive Algorithms for Collaborative Filtering , 1998, UAI.

[15]  Yuan-Hai Shao,et al.  A regularization for the projection twin support vector machine , 2013, Knowl. Based Syst..