Abstract
Personalised social matching systems can be seen as recommender systems that recommend people to others in the social networks. However, with the rapid growth of users in social networks and the information that a social matching system requires about the users, recommender system techniques have become insufficiently adept at matching users in social networks. This paper presents a hybrid social matching system that takes advantage of both collaborative and content-based concepts of recommendation. The clustering technique is used to reduce the number of users that the matching system needs to consider and to overcome other problems from which social matching systems suffer, such as cold start problem due to the absence of implicit information about a new user. The proposed system has been evaluated on a dataset obtained from an online dating website. Empirical analysis shows that accuracy of the matching process is increased, using both user information (explicit data) and user behavior (implicit data).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Harper, F.M., Sen, S., Frankowski, D.: Supporting social recommendations with activity-balanced clustering. Paper presented at the The 2007 ACM conference on Recommender systems (2007)
Oinas-Kukkonen, H., Lyytinen, K., Yoo, Y.: Social Networks and Information Systems: Ongoing and Future Research Streams. Journal of the Association for Information Systems 11(2), 3 (2010)
Terveen, L., McDonald, D.W.: Social matching: A framework and research agenda. ACM Transactions on Computer-Human Interaction (TOCHI) 12(3), 401–434 (2005)
Boyd, D.M., Ellison, N.B.: Social network sites: Definition, history, and scholarship. Journal of Computer-Mediated Communication 13(1), 210–230 (2008)
Eirinaki, M., Vazirgiannis, M.: Web mining for web personalization. ACM Transactions on Internet Technology 3(1), 1–27 (2003)
Grcar, M.: User Profiling: Web Usage Mining. In: Proceedings of the 7th International Multiconference Information Society IS (2004)
Lathia, N., Hailes, S., Capra, L.: Evaluating collaborative filtering over time. Paper presented at the Workshop on The Future of IR Evaluation (SIGIR), Boston (2009)
Mislove, A., Marcon, M., Gummadi, K.P., Druschel, P., Bhattacharjee, B.: Measurement and analysis of online social networks. Paper presented at the The 7th ACM SIGCOMM conference on Internet measurement (2007)
Mobasher, B.: Data mining for web personalization. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, p. 90. Springer, Heidelberg (2007)
Morgan, E.M., Richards, T.C., VanNess, E.M.: Comparing narratives of personal and preferred partner characteristics in online dating advertisements. Computers in Human Behavior 26(5), 883–888 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Alsaleh, S., Nayak, R., Xu, Y., Chen, L. (2011). Improving Matching Process in Social Network Using Implicit and Explicit User Information. In: Du, X., Fan, W., Wang, J., Peng, Z., Sharaf, M.A. (eds) Web Technologies and Applications. APWeb 2011. Lecture Notes in Computer Science, vol 6612. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20291-9_32
Download citation
DOI: https://doi.org/10.1007/978-3-642-20291-9_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-20290-2
Online ISBN: 978-3-642-20291-9
eBook Packages: Computer ScienceComputer Science (R0)