Context Model Based CF Using HMM for Improved Recommendation

Authors:
Jong-Hun Kim;Chang-Woo Song;Kyung-Yong Chung;Un-Gu Kang;Kee-Wook Rim;Jung-Hyun Lee
Affiliations:
Dept. of Computer Science Engineering, Inha University,;Dept. of Computer Science Engineering, Inha University,;School of Computer Information Engineering, Sangji University,;Dept. of Information Technology, Gachon University,;Dept. of Computer and Information Science, Sunmoon University, South Korea;Dept. of Computer Science Engineering, Inha University,
Venue:
PAKM '08 Proceedings of the 7th International Conference on Practical Aspects of Knowledge Management
Year:
2008

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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.