Abstract
With the explosive growth of the Internet, recommendation systems have been widely used by users. Collaborative Filtering (CF) is one of the most popular approaches for determining recommendations. With the development of Social Network Service(SNS), users will be influenced by his or her friends in the social network during the recommendation process. Accordingly considering the relationships(such as friendship, working relationship, kinship and so on) in Recommendation Algorithm(RA) is an important issue. Very little research, however, has focused on this issue. In this work, a Collaborative Filtering algorithm based on Social Network (SNS-CF )was proposed to filter and recommend items. The SNS-CF includes a Star sub-graph structure based recognition algorithm to cluster communities .This can ease data sparse. In another aspect, a Userrank method is used to calculate user‘s influence degree. This makes important user have greater weight in the recommendation. The experiment results demonstrate that the SNS-CF can improve the recommend precision.
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References
Francesco, R., Lior, R., Shapira, B., Kantor, P.B.: Recommender Systems Handbook, pp. 145–147. Springer (2011)
Min, G., Zhongfu, W., Feng, J.: Userrank for item-based collaborative filtering recommendation. Information Processing Letters 111(9), 440–446 (2011)
Liu, D.R., Tsai, P.Y., Chiu, P.H.: Personalized recommendation of popular Item articles for mobile applications. Information Sciences 181(9), 1552–1572 (2011)
Kwak, M., Cho, D.-S.: Collaborative filtering with automatic rating for recommendation. In: ISIE, pp. 625–628 (2001)
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Analysis of recommendation algorithms for e-commerce. In: Proceedings of the 2nd ACM Conference on Electronic Commerce, pp. 158–167 (2000)
Massa, P., Avesani, P.: Trust-aware bootstrapping of recommender systems. Citeseer 6, 29–33 (2006)
Newman, M.: The structure and function of complex networks. SIAM Review 45(2), 167–257 (2003)
Liu, Q., Liao, X.F., Wu, Y.: Novel clustering algorithm by using hypergraph based on granular computing. International Journal of Intelligent Systems 25(2), 155–164 (2010)
Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge Data Engineering 17(6), 734–749 (2005)
Zhang, H., Smith, M., Giles, C.L., Yen, J., Foley, H.: SNAKDD 2008 social network mining and analysis postworkshop report. ACM SIGKDD Explorations Newsletter 10(2), 74–77 (2008)
Oyanagi, S., Kubota, K., Nakase, A.: Application of matrix clustering to web log analysis and access prediction. In: WEBKDD, pp. 13–21 (2001)
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Liu, Q., Gao, Y., Peng, Z. (2012). A Novel Collaborative Filtering Algorithm Based on Social Network. In: Tan, Y., Shi, Y., Ji, Z. (eds) Advances in Swarm Intelligence. ICSI 2012. Lecture Notes in Computer Science, vol 7332. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31020-1_20
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DOI: https://doi.org/10.1007/978-3-642-31020-1_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31019-5
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