Web Page Recommendation Using A Stochastic
Process Model
Author(s)
B. J. Park, W. Choi & S. H. Noh
Abstract
Recommending Web pages based on the access patterns by previous visitors to a
user visiting some Web site can be helpful if they are is not familiar with the site.
To provide a proper recommendation service for users, it is necessary to
efficiently mine the Web page access patterns from a huge amount of Web server
log data. This paper presents an efficient Web page recommendation scheme
using a stochastic process model based on the time-homogeneous DTMC
(Discrete-Time Markov Chain). It views a series of Web page access as a
discrete-time stochastic process and constructs a state transition matrix based on
the relative frequency of moving from one page to another in the Web site. Then
it uses the initial state transition matrix to compute the probability for each Web
page to be accessed after some number of page references from the starting Web
page and selects a page with the largest probability value for recommendation.
Although this process involves a simple matrix multiplication, its computational
overhead could be expensive if the number of pages in the Web site is very large.
Our approach employs the PageGather algorithm to find clusters of closely
related ones among all the Web pages and uses each of these clusters as a state in
the state transition matrix. As a result, the size of the transition matrix can be
considerably reduced and so is the computation time without sacrificing the
effectiveness of recommendation too much. We demonstrate the effectiveness
and efficiency of our recommendation scheme with a series of experiments.
Keywords: Web mining, Web page recommendation, stochastic process model.
Keywords
Web mining, Web page recommendation, stochastic process model.
Related Book
Other papers in this volume
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