Abstract
Currently, recommender system becomes more and more important and challenging, as users demand higher recommendation quality. Collaborative tagging systems allow users to annotate resources with their own tags which can reflect users’ attitude on these resources and some attributes of resources. Based on our observation, we notice that there is co-occurrence effect of features, which may cause the change of user’s favor on resources. Current recommendation methods do not take it into consideration. In this paper, we propose an assistant and enhanced method to improve the performance of other methods by combining co-occurrence effect of features in collaborative tagging environment.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
A Survey of E-Commerce Recommender Systems (June 2007)
Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005)
Bennett, J., Lanning, S., Netflix, N.: The netflix prize. In: KDD Cup and Workshop in conjunction with KDD (2007)
Cai, Y., Leung, H.F., Li, Q., Tang, J., Li, J.: Tyco: Towards typicality-based collaborative filtering recommendation. In: ICTAI (2), pp. 97–104. IEEE Computer Society (2010)
Cai, Y., Li, Q., Xie, H., Yu, L.: Personalized Resource Search by Tag-Based User Profile and Resource Profile. In: Chen, L., Triantafillou, P., Suel, T. (eds.) WISE 2010. LNCS, vol. 6488, pp. 510–523. Springer, Heidelberg (2010)
Herlocker, J., Konstan, J., Borchers, A., Riedl, J.: An algorithmic framework for performing collaborative filtering. In: Proceedings of the 22nd ACM SIGIR Conference, August 15-19, pp. 230–237 (1999)
Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42, 30–37 (2009)
Melville, P., Mooney, R.J., Nagarajan, R.: Content-boosted collaborative filtering for improved recommendations. In: Proceedings of the Eighteenth National Conference on Artificial Intelligence, pp. 187–192 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Han, H., Cai, Y., Shao, Y., Li, Q. (2012). Improving Recommendation Based on Features’ Co-occurrence Effects in Collaborative Tagging Systems. In: Sheng, Q.Z., Wang, G., Jensen, C.S., Xu, G. (eds) Web Technologies and Applications. APWeb 2012. Lecture Notes in Computer Science, vol 7235. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29253-8_61
Download citation
DOI: https://doi.org/10.1007/978-3-642-29253-8_61
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-29252-1
Online ISBN: 978-3-642-29253-8
eBook Packages: Computer ScienceComputer Science (R0)