A framework to compute page importance based on user behaviors |
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| Authors: | Yuting Liu Tie-Yan Liu Bin Gao Zhiming Ma Hang Li |
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| Institution: | (1) School of Science, Beijing Jiaotong University, Beijing, China;(2) Microsoft Research Asia, Beijing, China;(3) Academy of Mathematical and Systems Science, CAS, Beijing, China |
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| Abstract: | This paper is concerned with a framework to compute the importance of webpages by using real browsing behaviors of Web users.
In contrast, many previous approaches like PageRank compute page importance through the use of the hyperlink graph of the
Web. Recently, people have realized that the hyperlink graph is incomplete and inaccurate as a data source for determining
page importance, and proposed using the real behaviors of Web users instead. In this paper, we propose a formal framework
to compute page importance from user behavior data (which covers some previous works as special cases). First, we use a stochastic
process to model the browsing behaviors of Web users. According to the analysis on hundreds of millions of real records of
user behaviors, we justify that the process is actually a continuous-time time-homogeneous Markov process, and its stationary
probability distribution can be used as the measure of page importance. Second, we propose a number of ways to estimate parameters
of the stochastic process from real data, which result in a group of algorithms for page importance computation (all referred
to as BrowseRank). Our experimental results have shown that the proposed algorithms can outperform the baseline methods such
as PageRank and TrustRank in several tasks, demonstrating the advantage of using our proposed framework. |
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