Abstract
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.
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References
Sohn, T., Li, K.A., Griswold, W.G., Hollan, J.: A Diary Study of Mobile Information Needs. In: CHI 2008 (2008)
Lamming, M.G., Newman, W.M.: Activity-Based Information Retrieval: Technology in Support of Human Memory. In: Personal Computers and Intelligent Systems (1992)
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)
Resnick, P., Varian, H.R.: Recommender systems. Communications of the ACM 40(3) (1997)
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)
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)
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)
Oliver, N., Horvitz, E., Garg, A.: Layered representations for human activity recognition. In: Fourth IEEE International Conference on Multimodal Interfaces (2002)
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)
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)
Begole, J.B., Tang, J.C., Hill, R.: Rhythm Modeling, Visualizations and Applications. In: Symposium on User Interface Software and Technology (2003)
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)
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)
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)
Ashbrook, D., Starner, T.: Learning significant locations and predicting user movement with GPS. In: Sixth International Symposium on Wearable Computers (2002)
Hinton, G.E.: Products of Experts. In: Ninth International Conference on Artificial Neural Networks (1999)
Eagle, N.: Machine Perception and Learning of Complex Social Systems. Ph.D. diss., Massachusetts Institute of Technology, Cambridge, MA (2005)
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Partridge, K., Price, B. (2009). Enhancing Mobile Recommender Systems with Activity Inference. In: Houben, GJ., McCalla, G., Pianesi, F., Zancanaro, M. (eds) User Modeling, Adaptation, and Personalization. UMAP 2009. Lecture Notes in Computer Science, vol 5535. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02247-0_29
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DOI: https://doi.org/10.1007/978-3-642-02247-0_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02246-3
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