Abstract
Document ranking and query model estimation can be considered as optimization problems. In this paper, we propose an iterative algorithm for optimizing query model and ranking function simultaneously in the context of language model and vector space model, respectively. This algorithm extends the risk minimization framework by incorporating manifold structure of word graph and document graph, and it provides a unified formulation of several existing heuristics for document ranking and query modeling. Moreover, we extend our algorithm by incorporating user’s true feedback information, and derive a new ranking model. Experimental results on four TREC collections show that our model is effective.
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Huang, Y., Sun, L., Nie, JY. (2010). A Unified Iterative Optimization Algorithm for Query Model and Ranking Refinement. 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_49
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DOI: https://doi.org/10.1007/978-3-642-17187-1_49
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