Abstract The performance of pseudo-relevance feedback technique is heavily dependent on two parameter values. Under the lack of relevance valuation results, these parameters can only rely on experience to set. In this paper, a pseudo-relevance feedback technique based on matrix factorization is proposed. This technique fuses multiple pseudo-relevance feedback results using the ideas of collaborative filtering together. And the optimal parameters are automatically selected for query expansion. Experimental results show that compared with the existing pseudo-relevance feedback techniques, the proposed method has a better retrieval performance, regardless of any underlying information retrieval model.
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2015, Vol. 28 
