Determining WWW User's Next Access and Its Application to Pre-fetching

World-Wide Web (WWW) services have grown to levels where significant delays are expected to happen. Techniques like pre-fetching are likely to help users to personalize their needs, reducing their waiting times. However, pre-fetching is only effective if the right documents are identified and if user's move is correctly predicted. Otherwise, pre-fetching will only waste bandwidth. Therefore, it is productive to determine whether a revisit will occur or not, before starting pre-fetching. In this paper we develop two user models that help determining user's next move. One model uses Random Walk approximation and the other is based on Digital Signal Processing techniques. We also give hints on how to use such models with a simple pre-fetching technique that we are developing.

Default ARTMAP

The default ARTMAP algorithm and its parameter values specified here define a ready-to-use general-purpose neural network system for supervised learning and recognition.

Default ARTMAP 2

Default ARTMAP combines winner-take-all category node activation during training , distributed activation during testing, and a set of default parameter values that define a ready-to-use, general-purpose neural network system for supervised learning and recognition. Winner-take-all ARTMAP learning is designed so that each input would make a correct prediction if re-presented immediately after its training presentation, passing the "next-input test." Distributed activation has been shown to improve test set prediction on many examples, but an input that made a correct winner-take-all prediction during training could make a different prediction with distributed activation. Default ARTMAP 2 introduces a distributed next-input test during training. On a number of benchmarks, this additional feature of the default system increases accuracy without significantly decreasing code compression. This paper includes a self-contained default ARTMAP 2 algorithm for implementation.