Abstract
The current collaborative recommendation approaches mainly measure users’ similarity by comparing user’s entire interests and don’t consider user’s interest quality, especially interest span. We propose a new approach to provide inter-website recommendation on proxy server based on partial similarity of interests, and construct corresponding user’s interest model to realize this method. According to psychological characteristic of interests, this approach divides user’s interest into several interest-points, which are correlated each other and farther divided into long-term interest and short-term interest. We mine the interest quality and correlation of interest-points from proxy log to construct user’s interest model. This method adopts different recommendation mechanism separately for long-term interests and short-term interests, which provides recommendation to target user’s long-term interests based on neighbors with partially similar interests and recommendation to user’s short-term interests based on experienced users. Experimental results indicate that this method can recommend interesting and unexpected inter-website pages to target users and improve the precision of personalized recommendation service on proxy server.
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
Chun, Z., Chun-xiao, X., Li-zhu, Z.: A Survey of Personalization Technology. Journal of Software 13(10), 1952–1961 (2002)
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 and Data Engineering 17(6), 734–749 (2005)
yi-qian, Y., ke-qin, K.: Personality Psychics. East China Normal University Press, Shanghai (1993)
Yan, G., Shuo, B., Zhi-feng, Y., Kai, Z.: Analyzing Scale of Web Logs and Mining Users’ Interests. Chinese Journal of Computers 28(9), 1483–1496 (2005)
Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: Grouplens: An open architecture for collaborative filtering of netnews. In: Proceeding of the ACM 1994 Conference on Computer Supported Cooperative Work, New York, pp. 175–186 (1994)
Breese, J.S., Heckerman, D., Kadie, C.: Empirical Analysis of Predictive Algorithms for Collaborative Filtering. In: Proceeding of the 14th Annual Conference on Uncertainty in Artificial Intelligence (UAI 1998), pp. 43–52. Morgan Kaufman, San Francisco (1998)
Wu, Y.-H., Chen, Y.-C., Chen, A.L.P.: Enabling personalized recommendation on the Web based on user interests and behaviors. In: The 11th Int. Workshop on Research Issues in Data Engineering (RIDE-DM 2001), Heidelberg, Germany, pp. 17–24 (2001)
Kamahara, J., Asakawa, T., Shimojo, S., Miyahara, H.: A Community-based Recommendation System to Reveal Unexpected Interests. In: Proceeding of the 11th International Multimedia Modeling Conference (MMM 2005), Melbourne, Australia, pp. 433–438 (2005)
Lou, W., Lu, H.: Efficient Proxy-based Web Access Prediction Service. In: Proceeding of the 11th International Conference on Information and Knowledge Management (CIKM 2002), McLean, Virginia, pp. 169–176 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yang, MH., Gu, ZM. (2006). Personalized Recommendation Based on Partial Similarity of Interests. In: Li, X., Zaïane, O.R., Li, Z. (eds) Advanced Data Mining and Applications. ADMA 2006. Lecture Notes in Computer Science(), vol 4093. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11811305_56
Download citation
DOI: https://doi.org/10.1007/11811305_56
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37025-3
Online ISBN: 978-3-540-37026-0
eBook Packages: Computer ScienceComputer Science (R0)