Predicting Web Actions from HTML Content

Most proposed Web prefetching techniques make predictions based on the historical references to requested objects. In contrast, this paper examines the accuracy of predicting a user's next action based on analysis of the content of the pages requested recently by the user. Predictions are made using the similarity of a model of the user's interest to the text in and around the hypertext anchors of recently requested Web pages. This approach can make predictions of actions that have never been taken by the user and potentially make predictions that reflect current user interests. We evaluate this technique using data from a full-content log of Web activity and find that textual similarity-based predictions outperform simpler approaches.

In Proceedings of the The Thirteenth ACM Conference on Hypertext and Hypermedia (HT'02), College Park, MD, June 11-15, 2002, pages 159-168.

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