Predicting users' requests on the WWW
UM '99 Proceedings of the seventh international conference on User modeling
Exploring Versus Exploiting when Learning User Models for Text Recommendation
User Modeling and User-Adapted Interaction
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
A Decision-Theoretic Approach for Pre-sending Information on the WWW
PRICAI '98 Proceedings of the 5th Pacific Rim International Conference on Artificial Intelligence: Topics in Artificial Intelligence
Letizia: an agent that assists web browsing
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Using Markov Chains for Link Prediction in Adaptive Web Sites
Soft-Ware 2002 Proceedings of the First International Conference on Computing in an Imperfect World
Extending Context Spaces Theory by Predicting Run-Time Context
NEW2AN '09 and ruSMART '09 Proceedings of the 9th International Conference on Smart Spaces and Next Generation Wired/Wireless Networking and Second Conference on Smart Spaces
Beyond the usual suspects: context-aware revisitation support
Proceedings of the 22nd ACM conference on Hypertext and hypermedia
Short Survey: A taxonomy of web prediction algorithms
Expert Systems with Applications: An International Journal
Client- and server-side revisitation prediction with SUPRA
Proceedings of the 2nd International Conference on Web Intelligence, Mining and Semantics
Hi-index | 0.00 |
Users' waiting time for information on the WWW may be reduced by pre-sending documents they are likely to request, albeit at a possible expense of additional transmission costs. In this paper, we describe a prediction model which anticipates the documents a user is likely to request next, and present a decision-theoretic approach for pre-sending documents based on the predictions made by this model. We introduce two evaluation methods which measure the immediate and the eventual benefit of pre-sending a document. We use these evaluation methods to compare the performance of our decision-theoretic policy to that of a naive pre-sending policy, and to identify the domain parameter configurations for which each of these policies provides a clear overall benefit to the user.