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Information Technology Journal

Year: 2013 | Volume: 12 | Issue: 22 | Page No.: 7004-7008
DOI: 10.3923/itj.2013.7004.7008
Using Key Users of Social Networks to Solve Cold Start Problem in Collaborative Recommendation Systems
Zhang Li, Qin Tao and Teng Piqiang

Abstract: With the application of collaborative filtering technologies and social network in personalized recommendation system, collaborative recommendation techniques based on social network are now made possible. This paper incorporates key users of social network into the traditional collaborative filtering algorithms to solve cold start problem. Also the influence of key users on recommendation accuracy is verified by experiments. Experimental results show that the key users can improve the accuracy of collaborative filtering algorithm which suggests that the key users can be used to alleviate the impact of cold start problem on the recommendation algorithm.

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How to cite this article
Zhang Li, Qin Tao and Teng Piqiang, 2013. Using Key Users of Social Networks to Solve Cold Start Problem in Collaborative Recommendation Systems. Information Technology Journal, 12: 7004-7008.

Keywords: Key users, social network, collaborative filtering and cold start

REFERENCES

  • Bauer, F. and J.T. Lizier, 2012. Identifying influential spreaders and efficiently estimating infection numbers in epidemic models: A walk counting approach. Europhysics Lett., Vol. 99.
    CrossRef    


  • De Meo, P., E. Ferrara, G. Fiumara and A. Provetti, 2011. Improving recommendation quality by merging collaborative filtering and social relationships. Proceedings of the 11th International Conference on Intelligent Systems Design and Applications, November 22-24, 2011, Cordoba, pp: 587-592.


  • Cacheda, F., V. Carneiro, D. Fernandez and V. Formoso, 2011. Comparison of collaborative filtering algorithms: Limitations of current techniques and proposals for scalable, high-performance recommender systems. ACM Trans. Web, 5: 2-33.
    CrossRef    Direct Link    


  • He, J. and W.W. Chu, 2010. A Social Network-Based Recommender System. Springer, US., pp: 47-74


  • Ahn, H.J., 2008. A new similarity measure for collaborative filtering to alleviate the new user Cold-starting problem. Inform. Sci., 178: 37-51.
    CrossRef    


  • Iaquinta, L. and G. Semeraro, 2011. Lightweight approach to the cold start problem in the video lecture recommendation. Proceedings of the ECML/PKDD Discovery Challenge Workshop, Volume 770, September 5, 2011, Athens, Greece, pp: 83-94.


  • Qiu, T., G. Chen, Z.K. Zhang and T. Zhou, 2011. An item-oriented recommendation algorithm on cold-start problem. EPL, Vol. 95.
    CrossRef    


  • Sahebi, S. and W.W. Cohen, 2011. Community-based recommendations: a solution to the cold start problem. Proceedings of the Workshop on Recommender Systems and the Social Web, October 23-27, 2011, Chicago, IL., USA -.


  • Schein, A.I., A. Popescul, L.H. Ungar and D.M. Pennock, 2002. Methods and metrics for Cold-start recommendations. Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, August 11-15, 2002, Finland, pp: 253-260.


  • Ting, S.D., H. Tao and Z.F. Hai, 2012. Survey of Cold-start problem in collaborative filtering recommender system. Comput. Modernization, 5: 59-63.


  • Sinha, R. and K. Swearingen, 2001. Comparing recommendations made by online systems and friends. Proceedings of the DELOS Workshop: Personalisation and Recommender Systems in Digital Libraries, Volume 106, June 18-20, 2001, Dublin, Ireland -.


  • Park, S.T. and W. Chu, 2009. Pairwise preference regression for cold-start recommendation. Proceedings of the 3rd ACM Conference on Recommender Systems, October 23-25, 2009, New York, pp: 21-28.


  • Su, X. and T.M. Khoshgoftaar, 2009. A survey of collaborative filtering techniques. Adv. Artificial Intell.,
    CrossRef    


  • Yuan, Q., S. Zhao, L. Chen, Y. Liu, S. Ding, X. Zhang and W. Zheng, 2009. Augmenting collaborative recommender by fusing explicit social relationships. Proceedings of the Workshop on Recommender Systems and the Social Web, Octobet 25, 2009, New York, pp: 49-56.


  • Zeng, A. and C.J. Zhang, 2013. Ranking spreaders by decomposing complex networks. Phys. Lett. A, 377: 1031-1035.
    CrossRef    


  • Zeng, W., M.S. Shang, Q.M. Zhang, L. Lue and T. Zhou, 2010. Can dissimilar users contribute to accuracy and diversity of personalized recommendation? Int. J. Modern Phys. C, 21: 1217-1227.
    CrossRef    


  • Ziegler, C.N. and G. Lausen, 2004. Analyzing Correlation between Trust and User Similarity in Online Communities. Springer, Berlin Heidelberg, pp: 251-265

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