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BrowseRank: letting web users vote for page importance

BrowseRank: letting web users vote for page importance,10.1145/1390334.1390412,Yu-ting Liu,Bin Gao,Tie-yan Liu,Ying Zhang,Zhiming Ma,Shuyuan He,Hang L

BrowseRank: letting web users vote for page importance   (Citations: 36)
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This paper proposes a new method for computing page importance, referred to as BrowseRank. The conventional approach to com- pute page importance is to exploit the link graph of the web and to build a model based on that graph. For instance, PageRank is such an algorithm, which employs a discrete-time Markov process as the model. Unfortunately, the link graph might be incomplete and inaccurate with respect to data for determining page impor- tance, because links can be easily added and deleted by web con- tent creators. In this paper, we propose computing page impor- tance by using a 'user browsing graph' created from user behav- ior data. In this graph, vertices represent pages and directed edges represent transitions between pages in the users' web browsing his- tory. Furthermore, the lengths of staying time spent on the pages by users are also included. The user browsing graph is more re- liable than the link graph for inferring page importance. This pa- per further proposes using the continuous-time Markov process on the user browsing graph as a model and computing the stationary probability distribution of the process as page importance. An e - cient algorithm for this computation has also been devised. In this way, we can leverage hundreds of millions of users' implicit voting on page importance. Experimental results show that BrowseRank indeed outperforms the baseline methods such as PageRank and TrustRank in several tasks.
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    • ...Later studies [17, 35, 4, 23, 36] often used 30 minutes timeout as the cut-off threshold.,Liu et al. [23] modeled user behaviors within sessions from web browsing logs as a continuous-time Markov process and proposed an algorithm named BrowseRank to improve performance of web page ranking.,The study described in this paper differs from previous work in that we focus on comparison of task, session, and query trails in terms of determining user satisfaction, predicting user search interests and generating query suggestions, rather than identifying session boundary [15, 17], extracting tasks from session [19, 24], or estimating web page relevance [30, 23, 36]...

    Zhen Liaoet al. Evaluating the effectiveness of search task trails

    • ...Click-based priors, which leverage the information about how frequently users visit a certain page to estimate its prior probability of relevance [21, 30], were also found to benefit web search...

    Michael Benderskyet al. Quality-biased ranking of web documents

    • ...PageRank (Brin and Page 1998; Page et al. 1998) and its variants (Boldi et al. 2005; Haveliwala 1999; Haveliwala and Kamvar 2003; Haveliwala et al. 2003; Haveliwala 2002; Langville and Meyer 2004; McSherry 2005; Richardson and Domingos 2002) compute page importance by taking the web as a graph of pages connected with hyperlinks...

    Bin Gaoet al. Page importance computation based on Markov processes

    • ...Most search engines include a ranking algorithm that computes a page’s authoritativeness based on either the link structure of the Web, such as HITS (Kleinberg 1999) and PageRank (Page et al. 1999; Brin and Page 1998), or mining of users’ browse histories, such as BrowseRank (Liu et al. 2008), Traffic-weighted Ranking (Meiss et al. 2008), and BookRank (Gonc ¸alves et al. 2009)...

    Bundit Manaskasemsaket al. Time-weighted web authoritative ranking

    • ...PageRank (Brin and Page 1998; Page et al. 1998) and its variants (Boldi et al. 2005; Haveliwala 1999; Haveliwala and Kamvar 2003; Haveliwala et al. 2003; Haveliwala 2002; Langville and Meyer 2004; McSherry 2005; Richardson and Domingos 2002) compute page importance by taking the web as a graph of pages connected with hyperlinks...

    Zhi-Ming MaHang Li. Page importance computation based on Markov processes

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