Efficient Data Update for Location-Based Recommendation Systems

  • Narin Jantaraprapa
  • Juggapong Natwichai
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7198)


Location-based recommendation systems are obtaining interests from the business and research communities. However, the efficiency of the update on the recommendation models is one of the most important issues. In this paper, we propose an efficient approach to update a recommendation model, User-centered collaborative location and activity filtering (UCLAF). The computational complexity of the model building is analyzed in details. Subsequently, our approach to update the models only the necessary parts is presented. As a result, the recommendation models obtained from our approach is exactly the same as the traditional re-calculation approach. The experiments have been conducted to evaluate our proposed approach. From the results, it is found that our proposed approach is highly efficient.


Execution Time User Similarity Space Cost Tensor Decomposition Recommendation Model 
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.


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  1. 1.
    Domeniconi, C., Gunopulos, D.: Incremental support vector machine construction. In: Proceedings of the 2001 IEEE International Conference on Data Mining, pp. 589–592. IEEE Computer Society, Washington, DC, USA (2001)CrossRefGoogle Scholar
  2. 2.
    Ganti, V., Gehrke, J., Ramakrishnan, R.: Demon–data evolution and monitoring. In: Proceedings of the 16th International Conference on Data Engineering (2000)Google Scholar
  3. 3.
    Resnick, P., Varian, H.R.: Recommender systems. Communication ACM 40, 56–58 (1997)CrossRefGoogle Scholar
  4. 4.
    Sarwar, B., Karypis, G., Konstan, J., Reidl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web, pp. 285–295. ACM, New York (2001)Google Scholar
  5. 5.
    Stoer, J., Bulirsch, R.: Introduction to numerical analysis. Texts in applied mathematics. Springer, Heidelberg (2002)CrossRefzbMATHGoogle Scholar
  6. 6.
    Zheng, V.W., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2010. AAAI Press (2010)Google Scholar
  7. 7.
    Zheng, V.W., Zheng, Y., Xie, X., Yang, Q.: Collaborative location and activity recommendations with gps history data. In: Proceedings of the 19th International Conference on World Wide Web, pp. 1029–1038. ACM, New York (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Narin Jantaraprapa
    • 1
  • Juggapong Natwichai
    • 1
  1. 1.Computer Engineering Department, Faculty of EngineeringChiang Mai UniversityChiang MaiThailand

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