Abstract

Modeling and predicting user surfing paths involves tradeoffs between model complexity and predictive accuracy. In this paper we explore predictive modeling techniques that attempt to reduce model complexity while retaining predictive accuracy. We show that compared to various Markov models, longest repeating subsequence models are able to significantly reduce model size while retaining the ability to make accurate predictions. In addition, sharp increases in the overall predictive capabilities of these models are achievable by modest increases to the number of predictions made. 1. Introduction Users surf the World Wide Web (WWW) by navigating along the hyperlinks that connect islands of content. If we could predict where surfers were going (that is, what they were seeking) we might be able to improve surfers' interactions with the WWW. Indeed, several research and industrial thrusts attempt to generate and utilize such predictions. These technologies include those for searching thro...

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