Location-Based Service with Context Data for a Restaurant Recommendation

  • Bae-Hee Lee
  • Heung-Nam Kim
  • Jin-Guk Jung
  • Geun-Sik Jo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4080)


Utilizing Global Positioning System (GPS) technology, it is possible to find and recommend restaurants for users operating mobile devices. For recommending restaurants, Personal Digital Assistants or cellular phones only consider the location of restaurants. However, a user’s background and environment information is assumed to be directly related to recommendation quality. In this paper, therefore, a recommender system using context information and a decision tree model for efficient recommendation is presented. This system considers location context, personal context, environment context, and user preference. Restaurant lists are obtained from location context, personal context, and environment context using the decision tree model. In addition, a weight value is used for reflecting user preferences. Finally, the system recommends appropriate restaurants to the mobile user. For this experiment, performance was verified using measurements such as k-fold cross-validation and Mean Absolute Error. As a result, the proposed system obtained an improvement in recommendation performance.


Global Position System Recommender System User Preference User Profile Environment Context 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Rashid, A.M., Albert, I., Cosley, D., Lam, S.K., McNee, S.M., Konstan, J.A., Riedl, J.: Getting to Know you: Learning New User Preferences in Recommender Systems. In: Proc. of the 7th Int. Conf. on Intelligent User Interfaces, pp. 127–134 (2002)Google Scholar
  2. 2.
    Cheverst, K., Davies, N., Mitchell, K., Friday, A., Efstratiou, C.: Developing a Context-aware Electronic Tourist Guide: Some Issues and Experiences. In: Proc. of the CHI 2000 Conf. on Human Factors in Computing System, pp. 17–24. ACM, New York (2000)CrossRefGoogle Scholar
  3. 3.
    Schopp, B., Ropnack, A., Markus, G.: The Need for Topological Time and Location in Mobile E-Business Applications. In: Proc. of the 9th Euromicro Workshop on Parallel and Distributed Processing (2001)Google Scholar
  4. 4.
    Tang, H., Soo, V.: A Personalized Restaurant Recommender Agent for Mobile E-Service. In: Proc. of the Conf. on E-Technology, E-Commerce and E-Service, March 2004, pp. 259–262. IEEE, Los Alamitos (2004)Google Scholar
  5. 5.
    Park, K., Lee, H.: Study on Family Restaurant Recommendation for Customers based on Benefit Sought and Demographical Variables. In: Proc. on The Korea Academic Society of Tourism and Leisure (2003)Google Scholar
  6. 6.
    Van Diggelen, F.: Indoor GPS Theory and Implementation. In: IEEE Position, Location & Navigation Symposium (2002)Google Scholar
  7. 7.
    Pazzani, M.J.: A Framework for Collaborative, Content-Based and Demographic Filtering. Artificial Intelligent Review (1999)Google Scholar
  8. 8.
    Claypool, M., Gokhale, A., Miranda, T., Murnikov, P., Netes, D., Sarti, M.: Combining Contents-Based and Collaborative Filters in an Online Newspaper. In: ACM SIGIR Workshop on Recommender Systems, Berkeley, CA (1999)Google Scholar
  9. 9.
    Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based Collaborative Filtering Recommendation Algorithms. In: Proc. of the Tenth International World Wide Web Conference on World Wide Web (2001)Google Scholar
  10. 10.
    Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann, San Francisco (2001)Google Scholar
  11. 11.
    Good, N., Schafer, B., Konstan, J., Borchers, A., Sarwar, B., Herlocker, J., Riedle, J.: Combining Collaborative Filtering with Personal Agents for Better Recommendation. In: Proc. of the AAAI conference, pp. 439–446 (1999)Google Scholar
  12. 12.
    Shardanand, U., Maes, P.: Social Information Filtering: Algorithms for Automating “Word of Mouth”. In: Proc. of the SIGCHI Conf. on Human Factors in Computing Systems (1995)Google Scholar
  13. 13.
    Dey, A.K., Abowd, G.D.: Towards a better understanding of Context and Context-Awareness. GVU Technical Report GITGVU-99-22, College of Computing, Georgia Institute of Technolgy 2, 2–14 (1999)Google Scholar
  14. 14.
    Chen, G., Kotz, D.: A Survey of Context-Aware Mobile Computing Research. Dartmouth Computer Science Technical Report TR2000-381 (2000)Google Scholar
  15. 15.
    Singh, S., Vajirkar, P., Lee, Y.: Context-Based Data Mining Using Ontologies. In: Song, I.-Y., Liddle, S.W., Ling, T.-W., Scheuermann, P. (eds.) ER 2003, vol. 2813, pp. 405–418. Springer, Heidelberg (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Bae-Hee Lee
    • 1
  • Heung-Nam Kim
    • 1
  • Jin-Guk Jung
    • 1
  • Geun-Sik Jo
    • 2
  1. 1.Intelligent E-Commerce Systems Laboratory, Department of Computer Science & Information EngineeringInha University 
  2. 2.School of Computer Science & EngineeringInha UniversityIncheonKorea

Personalised recommendations