Adaptive Personalization for Mobile Content Delivery
Abstract
While personalization has proved to be an important supplement to web applications, the constraints of mobile information access make personalization essential to producing usable applications. Mobile devices, such as cell phones or personal digital assistants, have much smaller screens, more limited input capabilities, slower and less reliable network connections, less memory and less processing power than desktop computers. We discuss an adaptive personalization technology that automatically delivers personalized information available to the mobile user via wireless or wired synchronization on platforms such as AvantGo or Qualcomm's BREW™. Both of these platforms have capabilities not available in most browsers for wireless devices. In particular, they allow for local storage of some content on the mobile device that may be accessed without wireless connectivity. Our applications attempt to optimize the batch download of information to wireless devices so that the delays and costs associated with interactive browsing are reduced. We present evidence that the personalization algorithm increases the usage of mobile content applications by displaying personally relevant information to individual users.
References (11)
- Billsus, D., & Pazzani, M. (1998). Learning collaborative information filters. Proceedings of the Fifteenth International Conference on Machine Learning (pp. 46-54). Madison, WI: Morgan Kaufmann.
- Billsus, D., & Pazzani, M. (2000). User Modeling for Adaptive News Access. User Modeling and User-Adapted Interaction, 10(2/3): 147-180.
- Buchanan, G., Farrant, S., Jones, M., Marsden, G. Pazzani, M &, Thimbleby. H. (2001). Improving Mobile Internet Usability. In Proceedings of the Tenth International World Wide Web Conference, Hong Kong, pp. 673-680.
- Cheverst, K., Mitchell, K., and Davies, N. (2002). The role of adaptive hypermedia in a context- aware tourist GUIDE. Communications of the ACM, 45, 5.
- Claypool, M., Le, P., Wased, M. and Brown, D. (2001). Implicit interest indicators. Proceedings of the International Conference on Intelligent User Interfaces, Santa Fe, 33-40.
- Cover, T. Hart, P. (1967). Nearest Neighbor pattern classification, IEEE Transactions on Information Theory, 13, pp. 21-27.
- Duda, R. & Hart, P. (1973). Pattern Classification and Scene Analysis. New York, NY: Wiley and Sons.
- Kobsa, A. (2002). Personalized hypermedia and privacy. Communications of the ACM, 45, 5.
- Pazzani, M. (1999). A Framework for Collaborative, Content-Based and Demographic Filtering. Artificial Intelligence Review, 13(5-6): 393-408
- Pazzani M., & Billsus, D. (1997). Learning and Revising User Profiles: The identification of interesting web sites. Machine Learning, 27, 313-331.
- Yang, Y. (1999). An evaluation of statistical approaches to text categorization. Journal of Information Retrieval, Vol 1, No. 1/2, pp. 67-88.