Random walk based rank aggregation to improving web search

Lin LI, Guandong XU, Yanchun ZHANG, Masaru KITSUREGAWA

Research output: Contribution to journalArticlespeer-review

17 Citations (Scopus)


In Web search, with the aid of related query recommendation, Web users can revise their initial queries in several serial rounds in pursuit of finding needed Web pages. In this paper, we address the Web search problem on aggregating search results of related queries to improve the retrieval quality. Given an initial query and the suggested related queries, our search system concurrently processes their search result lists from an existing search engine and then forms a single list aggregated by all the retrieved lists. We specifically propose a generic rank aggregation framework which consists of three steps. First we build a so-called Win/Loss graph of Web pages according to a competition rule, and then apply the random walk mechanism on the Win/Loss graph. Last we sort these Web pages by their ranks using a PageRank-like rank mechanism. The proposed framework considers not only the number of wins that an item won in competitions, but also the quality of its competitor items in calculating the ranking of Web page items. Experimental results show that our search system can clearly improve the retrieval quality in a parallel manner over the traditional search strategy that serially returns result lists. Moreover, we also provide empirical evidences as to demonstrate how different rank aggregation methods affect the retrieval quality. Copyright © 2011 Elsevier B.V. All rights reserved.

Original languageEnglish
Pages (from-to)943-951
JournalKnowledge-Based Systems
Issue number7
Early online dateApr 2011
Publication statusPublished - Oct 2011


Li, L., Xu, G., Zhang, Y., & Kitsuregawa, M. (2011). Random walk based rank aggregation to improving web search. Knowledge-Based Systems, 24(7), 943-951. https://doi.org/10.1016/j.knosys.2011.04.001


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