Applying Associative Relationship on the Clickthrough Data to Improve Web Search

Abstract

The performance of web search engines may often deteriorate due to the diversity and noise contained within web pages. Some methods proposed to use clickthrough data to achieve more accurate information for web pages as well as improve the search performance. However, sparseness became the great challenge in exploiting clickthrough data. In this paper, we propose a novel algorithm to exploit the user clickthrough data. It first explores the relationship between queries and web pages to mine out co-visiting as the associative relationship among the Web pages, and then Spreading Activation mechanism is used to re-rank the results of Web search. Our approach could alleviate such sparseness and the experimental results on a large set of MSN clickthrough log data show a significant improvement on search performance over the DirectHit algorithm as well as the baseline search engine.KeywordsSearch PerformanceQuery ExpansionBibliographic CouplingAnchor TextAssociative RelationshipThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.