Abstract
Query expansion technologies based on pseudo-relevance documents have been proven to be effective in many information retrieval tasks. One problem with these methods is that some of the expansion terms extracted from feedback documents are irrelevant to the query, which may hurt the retrieval performance. In this paper, we proposed a normalized weight SimRank (NWS) approach for query expansion, with query logs collected by a practical search engine. Analyzing the relationship between queries and URLs, we create a query-click graph, and a term-relationship graph is constructed by several transformations. In order to reduce the computational complexity of NWS, strategies of pruning and radius limit were used to optimize the algorithm. Experimental results on two TREC test collections show that our approach can discover the qualified terms effectively and improve queries’ accuracy.
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Ma, Y., Lin, H., Jin, S. (2010). A Revised SimRank Approach for Query Expansion. In: Cheng, PJ., Kan, MY., Lam, W., Nakov, P. (eds) Information Retrieval Technology. AIRS 2010. Lecture Notes in Computer Science, vol 6458. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17187-1_53
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DOI: https://doi.org/10.1007/978-3-642-17187-1_53
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