Abstract
Social tagging system has become a hot research topic due to the prevalence of Web2.0 during the past few years. These systems can provide users effective ways to collaboratively annotate and organize items with their own tags. However, the flexibility of annotation brings with large numbers of redundant tags. It is a very difficult task to find users’ interest exactly and recommend proper friends to users in social tagging systems. In this paper, we propose a Friend Recommendation algorithm by User similarity Graph (FRUG) to find potential friends with the same interest in social tagging systems. To alleviate the problem of tag redundancy, we utilize Latent Dirichlet Allocation (LDA) to obtain users’ interest topics. Moreover, we propose a novel multiview users’ similarity measure method to calculate similarity from users’ interest topics, co-collected items and co-annotated tags. Then, based on the users’ similarities, we build user similarity graph and make interest-based user recommendation by mining the graph. The experimental results on tagging dataset of Delicious validate the good performance of FRUG in terms of precision and recall.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Shepitsen, A., Gemmell, J., Mobasher, B., Burke, R.: Personalized recommendation in social tagging systems using hierarchical clustering. In: Proceedings of the 2008 ACM Conference on Recommender Systems, pp. 259–266. ACM, New York (2008)
Nowell, L.D., Kleinberg, J.: The link prediction problem for social networks. In: Proceedings of the Twelfth International Conference on Information and Knowledge Management (CIKM), pp. 1019–1031. ACM, New York (2004)
Scellato, S., Noulas, A., Mascolo, C.: Exploiting place features in link prediction on location-based social networks. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1046–1054. ACM, New York (2011)
Symeonidis, P., Tiakas, E., Manolopoulos, Y.: Transitive node similarity for link prediction in social networks with positive and negative links. In: Proceedings of the Fourth ACM Conference on Recommender Systems (RecSys 2010), pp. 183–190. ACM, New York (2010)
Hsu, W.H., King, A.L., Paradesi, M.S., Pydimarri, T., Weninger, T.: Collaborative and structural recommendation of friends using weblog-based social network analysis. In: AAAI Spring Symposium: Computational Approaches to Analyzing Weblogs, pp. 55–60. AAAI, Menlo Park (2006)
Xie, X.: Potential friend recommendation in online social network. In: Proceedings of the IEEE/ACM International Conference on Green Computing and Communications and International Conference on Cyber, Physical and Social Computing, pp. 831–835. IEEE, Piscataway (2010)
Li, Q., Zheng, Y., Xie, X., Chen, Y., Liu, W., Ma, W.Y.: Mining user similarity based on location history. In: Proceedings of the 16th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 34–42. ACM, New York (2008)
Wartena, C., Brussee, R., Wibbels, M.: Using tag co-occurrence for recommendation. In: Intelligent Systems Design and Applications (ISDA 2009), pp. 273–278. IEEE, Piscataway (2009)
Zhou, T.C., Ma, H., Lyu, M.R., King, I.: UserRec: a user recommendation framework in social tagging systems. In: Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence, pp. 1486–1491. AAAI, Washington (2010)
Feng, W., Wang, J.: Incorporating heterogeneous information for personalized tag recommendation in social tagging systems. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1276–1284. ACM. New York (2012)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Zhang, Z.F., Li, Q.D.: Latent friend recommendation in social network services. J. China Soc. Sci. Tech. Inf. 30, 1319–1325 (2011)
Cantador, I., Brusilovsky, P.: 2nd Workshop on Information Heterogeneity and Fusion in Recommender Systems. In: Proceedings of the 5th ACM Conference on Recommender Systems (2011)
Delicious dataset form the website Grouplens. http://grouplens.org/datasets/hetrec-2011/
Zhang, Z.K., Zhou, T., Zhang, Y.C.: Personalized recommendation via integrated diffusion on user–item–tag tripartite graphs. Phys. A Stat. Mech. Appl. 389, 179–186 (2010)
Weng, J., Lim, E.P., Jiang, J., He, Q.: Twitterrank: finding topic-sensitive influential twitterers. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining, pp. 261–270. ACM, New York (2010)
Pennacchiotti, M., Gurumurthy, S.: Investigating topic models for social media user recommendation. In: Proceedings of the 20th International Conference Companion on World Wide Web, pp. 101–102. ACM, New York (2011)
Acknowledgement
This work was partially supported by the National Natural Science Foundation of China (NSFC) projects No. 61202296, the National High-Technology Research and Development Program (“863” program) of China under Grant No. 2013AA01A212, the Natural Science Foundation of Guangdong Province project No. S2012030006242 and the Key Areas of Guangdong-HongKong Breakthrough project No. 2012A090200008.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Wu, BX., Xiao, J., Chen, JM. (2015). Friend Recommendation by User Similarity Graph Based on Interest in Social Tagging Systems. In: Huang, DS., Han, K. (eds) Advanced Intelligent Computing Theories and Applications. ICIC 2015. Lecture Notes in Computer Science(), vol 9227. Springer, Cham. https://doi.org/10.1007/978-3-319-22053-6_41
Download citation
DOI: https://doi.org/10.1007/978-3-319-22053-6_41
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-22052-9
Online ISBN: 978-3-319-22053-6
eBook Packages: Computer ScienceComputer Science (R0)