Enhancing Mobile Recommender Systems with Activity Inference

  • Kurt Partridge
  • Bob Price
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5535)


Today’s mobile leisure guide systems give their users unprecedented help in finding places of interest. However, the process still requires significant user interaction, for example to specify preferences and navigate lists. While interaction is effective for obtaining desired results, learning the interaction pattern can be an obstacle for new users, and performing it can slow down experienced users. This paper describes how to infer a user’s high-level activity automatically to improve recommendations. Activity is determined by interpreting a combination of current sensor data, models generated from historical sensor data, and priors from a large time-use study. We present an initial user study that shows an increase in prediction accuracy from 62% to over 77%, and discuss the challenges of integrating activity representations into a user model.


Bayesian Inference recommender systems activity inference 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Sohn, T., Li, K.A., Griswold, W.G., Hollan, J.: A Diary Study of Mobile Information Needs. In: CHI 2008 (2008)Google Scholar
  2. 2.
    Lamming, M.G., Newman, W.M.: Activity-Based Information Retrieval: Technology in Support of Human Memory. In: Personal Computers and Intelligent Systems (1992)Google Scholar
  3. 3.
    Baus, J., Cheverst, K., Kray, C.: A survey of map-based mobile guides. In: Zipf, A., Meng, L., Reichenbacher, T. (eds.) Map based mobile services - Theories, Methods and Implementations. Springer, Heidelberg (2005)Google Scholar
  4. 4.
    Resnick, P., Varian, H.R.: Recommender systems. Communications of the ACM 40(3) (1997)Google Scholar
  5. 5.
    Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge And Data Engineering 17(6) (2005)Google Scholar
  6. 6.
    Poslad, S., Laamanen, H., Malaka, R., Nick, A., Buckle, P., Zipf, A.: CRUMPET: Creation of user-friendly mobile services personalised for tourism. In: 3G, London (2001)Google Scholar
  7. 7.
    van Setten, M., Pokraev, S., Koolwaaij, J.: Context-Aware Recommendations in the Mobile Tourist Application COMPASS. In: De Bra, P.M.E., Nejdl, W. (eds.) AH 2004. LNCS, vol. 3137, pp. 235–244. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  8. 8.
    Oliver, N., Horvitz, E., Garg, A.: Layered representations for human activity recognition. In: Fourth IEEE International Conference on Multimodal Interfaces (2002)Google Scholar
  9. 9.
    Kern, N., Schiele, B., Schmidt, A.: Multi-sensor Activity Context Detection for Wearable Computing. In: Aarts, E., Collier, R.W., van Loenen, E., de Ruyter, B. (eds.) EUSAI 2003. LNCS, vol. 2875, pp. 220–232. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  10. 10.
    Liao, L., Fox, D., Kautz, H.: Extracting Places and Activities from GPS traces Using Hierarchical Conditional Random Fields. The International Journal of Robotics Research (2007)Google Scholar
  11. 11.
    Begole, J.B., Tang, J.C., Hill, R.: Rhythm Modeling, Visualizations and Applications. In: Symposium on User Interface Software and Technology (2003)Google Scholar
  12. 12.
    Krumm, J., Horvitz, E.: Predestination: Inferring Destinations from Partial Trajectories. In: Dourish, P., Friday, A. (eds.) UbiComp 2006. LNCS, vol. 4206, pp. 243–260. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  13. 13.
    Bellotti, V., Begole, J., Chi, E.H., Ducheneaut, N., Fang, J., Isaacs, E., King, T., Newman, M.W., Partridge, K., Price, B., Rasmussen, P., Roberts, M., Schiano, D.J., Walendowski, A.: Activity-Based Serendipitous Recommendations with the Magitti Mobile Leisure Guide. In: CHI 2008 (2008)Google Scholar
  14. 14.
    Ducheneaut, N., Partridge, K., Huang, Q., Price, B., Roberts, M., Chi, E., Bellotti, V., Begole, B.: Collaborative Filtering Is Not Enough? Experiments with a Mixed-Model Recommender for Leisure Activities. In: User Modeling, Adaptation, and Personalization (2009)Google Scholar
  15. 15.
    Ashbrook, D., Starner, T.: Learning significant locations and predicting user movement with GPS. In: Sixth International Symposium on Wearable Computers (2002)Google Scholar
  16. 16.
    Hinton, G.E.: Products of Experts. In: Ninth International Conference on Artificial Neural Networks (1999)Google Scholar
  17. 17.
    Eagle, N.: Machine Perception and Learning of Complex Social Systems. Ph.D. diss., Massachusetts Institute of Technology, Cambridge, MA (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Kurt Partridge
    • 1
  • Bob Price
    • 1
  1. 1.Palo Alto Research CenterPalo AltoUSA

Personalised recommendations