Learning Web Request Patterns

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Learning Web Request Patterns (2004)

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by Brian D. Davison

Venue:	Web Dynamics: Adapting to Change in Content, Size, Topology and Use
Citations:	11 - 1 self

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@MISC{Davison04learningweb,
    author = {Brian D. Davison},
    title = {Learning Web Request Patterns},
    year = {2004}
}

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Abstract

Summary. Most requests on the Web are made on behalf of human users, and like other human-computer interactions, the actions of the user can be characterized by identifiable regularities. Much of these patterns of activity, both within a user, and between users, can be identified and exploited by intelligent mechanisms for learning Web request patterns. Our focus is on Markov-based probabilistic techniques, both for their predictive power and their popularity in Web modeling and other domains. Although history-based mechanisms can provide strong performance in predicting future requests, performance can be improved by including predictions from additional sources. In this chapter we review the common approaches to learning and predicting Web request patterns. We provide a consistent description of various algorithms (often independently proposed), and compare performance of those techniques on the same data sets. We also discuss concerns for accurate and realistic evaluation of these techniques. 1

Keyphrases

web request pattern future request strong performance human user human-computer interaction various algorithm realistic evaluation identifiable regularity predictive power intelligent mechanism history-based mechanism web modeling data set additional source consistent description compare performance common approach markov-based probabilistic technique

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