Abstract
As the world-wide-web grows rapidly and a user’s browsing experiences are needed to be personalized, the problem of predicting a user’s behavior on a web-site has become important. We present a probability model to utilize path profiles of users from web logs to predict the user’s future requests. Each of the user’s next probable requests is given a conditional probability value, which is calculated according to the function presented by us. Our model can give several predictions ranked by the values of their probability instead of giving one, thus increasing recommending ability. The experiments show that our algorithm and model has a good performance. The result can potentially be applied to a wide range of applications on the web.
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Wu, S., Wang, W. (2004). Predicting Web Requests Efficiently Using a Probability Model. In: Dai, H., Srikant, R., Zhang, C. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2004. Lecture Notes in Computer Science(), vol 3056. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24775-3_67
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DOI: https://doi.org/10.1007/978-3-540-24775-3_67
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22064-0
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