Abstract
Query expansion is a commonly used technique to address the problem of short and under-specified search queries in information retrieval. Traditional query expansion frameworks return static results, whereas user’s information needs is dynamics in nature. User’s search goal, even for the same query, may be different at different instances. This often leads to poor coherence between traditional query expansion and user’s search goal resulting poor retrieval performance. In this study, we observe that user’s search pattern is influenced by his/her recent searches in many search instances. We further propose a query expansion framework which explores user’s real time implicit feedback provided at the time of search to determine user’s search context and identify relevant query expansion terms. From extensive experiments, it is evident that the proposed query expansion framework adapts to the changing needs of user’s information need.
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Singh, S.R., Murthy, H.A., Gonsalves, T.A. (2013). Inference Based Query Expansion Using User’s Real Time Implicit Feedback. In: Fred, A., Dietz, J.L.G., Liu, K., Filipe, J. (eds) Knowledge Discovery, Knowledge Engineering and Knowledge Management. IC3K 2010. Communications in Computer and Information Science, vol 272. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29764-9_11
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DOI: https://doi.org/10.1007/978-3-642-29764-9_11
Publisher Name: Springer, Berlin, Heidelberg
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