Abstract
Users in ubiquitous environments can use dynamic services whenever and wherever they are located because these environments connect objects and users through wire and wireless networks. Also, there are many devices and services in these environments. However, it is difficult to effectively use conventional filtering method of the recommendation system in future ubiquitous environments because it does not reflect context information well in these environments. This paper attempt to define context model and propose new Collaborative Filtering (CF) based on Hidden Markov Models (HMMs) that are trained by context information. The Collaborative Filtering using HMMs (CFH) is suited to a user’s interests and preferences. The Ubiquitous Recommendation System (URS) used in this study based on CFH uses an Open Service Gateway Initiative (OSGi) framework to recognize context information and connect device in smart home.
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
Preview
Unable to display preview. Download preview PDF.
References
Kim, J.H., Jung, K.J., Lee, J.H.: Hybrid Music Filtering for Recommendation Based Ubiquitous Computing Environment. In: Greco, S., Hata, Y., Hirano, S., Inuiguchi, M., Miyamoto, S., Nguyen, H.S., Słowiński, R. (eds.) RSCTC 2006. LNCS (LNAI), vol. 4259, pp. 796–805. Springer, Heidelberg (2006)
Balabanovic, M., Shoham, Y.: Fab: Content-based, Collaborative Recommendation. Communication of the Association of Computing Machinery 40(3), 66–72 (1997)
Jung, K.Y., Lee, J.H.: User Preference Mining through Hybrid Collaborative Filtering and Content-based Filtering in Recommendation System. IEICE Transaction on Information and Systems E87-D(12), 2781–2790 (2004)
Chen, H.–C., Chen, A.L.P.: A music recommendation system based on music data grouping and user interests. In: Proc. of the CIKM 2001, pp. 231–238 (2001)
Brown, P.J., Bovey, J.D., Chen, X.: Context-Aware Application: From the Laboratory to the Marketplace. IEEE Personal Communication, 58–64 (1997)
Rabiner, L.R.: A Tutorial on Hidden Markov Models and Selected Application in Speech Recognition. Proc. IEEE 77(2), 257–286 (1989)
Breese, J.S., Heckerman, D., Kadie, C.: Empirical Analysis of Predictive Algorithms for Collaborative Filtering. In: Proc. of the 14th Conference on Uncertainty in AI (1998)
Herlocker, J., et al.: An Algorithm Framework for Performing Collaborative Filtering. In: Proc. of ACM SIGIR 1999 (1999)
Resnick, P., et al.: GroupLens: An Open Architecture for Collaborative Filtering of Netnews. In: Proc. of ACM CSCW 1994, pp. 175–186 (1994)
Jung, K.Y., Lee, J.H.: Prediction of User Preference in Recommendation System using Association User Clustering and Bayesian Estimated Value. In: McKay, B., Slaney, J.K. (eds.) Canadian AI 2002. LNCS (LNAI), vol. 2557, pp. 284–296. Springer, Heidelberg (2002)
Dobrev, P., Famolari, D., Kurzke, C., Miller, B.A.: Device and Service Discovery in Home Networks with OSGi. IEEE Communications Magazine 40(8), 86–92 (2002)
Bellavista, P., Corradi, A., Stefanelli, C.: Mobile Agent Middleware for Mobile Computing. IEEE Computer 34(3) (2001)
Liu, T., Martonosi, M.: Impala: A Middleware System for Managing Autonomic, Parallel Sensor Systems. In: ACM SIGPLAN Symp. Principles and Practice of Parallel Programming (2003)
Gu, T., Pung, H.K., Zhang, D.Q.: An Ontology-based Context Model in Intelligent Environments. In: Proc. of Communication Networks and Distributed Systems Modeling and Simulation Conference, pp. 270–275 (2004)
Romer, K., Schoch, T., Mattern, F., Dubendorfer, T.: Smart Identification Frameworks for Ubiquitous Computing Application. In: IEEE International Conference on Pervasive Computing and Communication (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kim, JH., Song, CW., Chung, KY., Kang, UG., Rim, KW., Lee, JH. (2008). Context Model Based CF Using HMM for Improved Recommendation. In: Yamaguchi, T. (eds) Practical Aspects of Knowledge Management. PAKM 2008. Lecture Notes in Computer Science(), vol 5345. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89447-6_25
Download citation
DOI: https://doi.org/10.1007/978-3-540-89447-6_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-89446-9
Online ISBN: 978-3-540-89447-6
eBook Packages: Computer ScienceComputer Science (R0)