Item recommendation based on context-aware model for personalized u-healthcare service

A personalized service in the ubiquitous environment is to provide services or items, which reflect personal tastes, attitudes, and contexts. It is impossible to reflect the context information generated in u-healthcare environments due to the existing recommendation system performing the recommendation using the information directly input by users and application usage record only. This study develops a context-aware model using the context information provided by the context information model. The study applies it to the extraction of the missing value in a collaborative filtering process. The context-aware model reflects the information that selects items by users according to the appropriate context using the C-HMM and provides it to users. The solution of the missing value in the preference significantly affects the recommendation accuracy in a preference based item supply method. Thus, this study developed a new collaborative filtering for ubiquitous environments by reflecting the missing preference value and reflecting it to the collaborative filtering using the context-aware model. Also, the validity of this method will be evaluated by applying it to menu services in u-healthcare services.

[1]  S. Venkatesan,et al.  Accelerometer-based human abnormal movement detection in wireless sensor networks , 2007, HealthNet '07.

[2]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[3]  Woontack Woo,et al.  Ubi-UCAM: A Unified Context-Aware Application Model , 2003, CONTEXT.

[4]  Li Gong,et al.  A Software Architecture for Open Service Gateways , 2001, IEEE Internet Comput..

[5]  Dean P. Foster,et al.  Clustering Methods for Collaborative Filtering , 1998, AAAI 1998.

[6]  John Riedl,et al.  Combining Collaborative Filtering with Personal Agents for Better Recommendations , 1999, AAAI/IAAI.

[7]  David Heckerman,et al.  Empirical Analysis of Predictive Algorithms for Collaborative Filtering , 1998, UAI.

[8]  Mark S. Ackerman,et al.  Expertise recommender: a flexible recommendation system and architecture , 2000, CSCW '00.

[9]  Peter J. Brown,et al.  Context-aware applications: from the laboratory to the marketplace , 1997, IEEE Wirel. Commun..

[10]  Kee-Wook Rim,et al.  Development of Design Recommender System Using Collaborative Filtering , 2003, ICADL.

[11]  Michael L. Littman,et al.  Activity Recognition from Accelerometer Data , 2005, AAAI.

[12]  Yoav Shoham,et al.  Fab: content-based, collaborative recommendation , 1997, CACM.

[13]  Changseok Bae,et al.  User activity recognition and logging in distributed Intelligent Gadgets , 2008, 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems.

[14]  Jung-Hyun Lee,et al.  User Preference Mining through Hybrid Collaborative Filtering and Content-Based Filtering in Recommendation System , 2004, IEICE Trans. Inf. Syst..

[15]  Lawrence R. Rabiner,et al.  A tutorial on Hidden Markov Models , 1986 .

[16]  Christian Wojek,et al.  Activity Recognition and Room-Level Tracking in an Office Environment , 2006, 2006 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems.