Skip to main content
Springer Nature Link
Log in

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

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Balabanovic M, Shoham Y (1997) Fab: content-based, collaborative recommendation. Communication of the Association of Computing Machinery 40(3):66–72

    Article  Google Scholar 

  2. Breese JS, Kadie C (1998) Empirical analysis of predictive algorithms for collaborative filtering. Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, Madison, WI, pp. 43–52

  3. Brown PJ, Bovey JD, Chen X (1997) Context-aware application: From the laboratory to the marketplace. IEEE Personal Communication, pp. 58–64

  4. Burchfield TR, Venkatesan S (2007) Accelerometer-based human abnormal movement detection in wireless sensor networks. In the 1st ACM SIGMOBILE international workshop on Systems and networking support for healthcare and assisted living environments San Juan, Puerto Rico

  5. Gong L (2001) A software architecture for open service gateways. IEEE Internet Computing 5(1):64–70

    Article  Google Scholar 

  6. Good N, Schafer JB, Konstan JA, Borchers A, Sarwar B, Herlocker J, Riedl J (1999) Combining collaborative filtering with personal agents for better recommendations. Proceedings of the National Conference on Artificial Intelligence, pp. 439–446

  7. Hidoc, http://www.hidoc.co.kr/HealthPedia

  8. Jang S, Woo W (2003) ubi-UCAM: A unified context-aware application model. Context03, pp. 178–189

  9. Jeong K, Won J, Bae C (2008) User activity recognition and logging in distributed Intelligent Gadgets. In IEEE International Conferenceon Multi sensor Fusion and Integration for Intelligent Systems

  10. Jung KY, Lee JH (2004) 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

    Google Scholar 

  11. Jung KY, Choi JH, Rim KW, Lee JH (2003) Development of design recommender system using collaborative filtering. LNCS 2911, Proceedings of the 6th International Conference of Asian Digital Libraries (ICADL'03), pp. 100–110

  12. McDonald DW, Mark SA (2000) Expertise recommender: a Flexible recommendation system and architecture. Proceedings of the ACM Conference on Computer Supported Cooperative Work, pp. 231–240

  13. New Life Hypertension & Diabetes Clinic, http://www.goliving.co.kr

  14. Rabiner LR (1989) A tutorial on hidden Markov models and selected application in speech recognition. In Proc IEEE 77(2):257–286

  15. Ravi N, Dandekar N, Mysore P, Littman ML (2005) Activity recognition from accelerometer data. In American Association for Artificial Intelligence, pp. 11541–1546

  16. Ungar LH, Foster DP (1998) Clustering methods for collaborative filtering. Proceedings of the Workshop on Recommendation Systems, pp. 114-129

  17. Welltizen, http://neps.welltizen.com

  18. Wojek C, Nickel K, Stiefelhagen R (2006) Activity recognition and room-level tracking in an office environment. in IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems Heidelberg, Germany

Download references

Acknowledgment

This research was supported by Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education, Science and Technology. (No. 2011-0008934).

Author information

Authors and Affiliations

  1. U-Healthcare Department, BIT Computer Co., Ltd, Seocho-Dong, Seocho-Gu, Seoul, Korea

    Jonghun Kim

  2. Department of Industrial Security, Kyonggi University, Seoul, South Korea

    Daesung Lee

  3. School of Computer Information Engineering, Sangji University, Wonju, South Korea

    Kyung-Yong Chung

Authors
  1. Jonghun Kim
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Daesung Lee
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Kyung-Yong Chung
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Kyung-Yong Chung.

Additional information

This paper is significantly revised from an earlier version presented at the International Conference on Information Science and Applications 2011.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kim, J., Lee, D. & Chung, KY. Item recommendation based on context-aware model for personalized u-healthcare service. Multimed Tools Appl 71, 855–872 (2014). https://doi.org/10.1007/s11042-011-0920-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-011-0920-0

Keywords