Abstract

Pseudo feedback is a commonly used technique to improve information retrieval performance. It assumes a few top-ranked documents to be relevant, and learns from them to improve the retrieval accuracy. A serious problem is that the performance is often very sensitive to the number of pseudo feedback documents. In this poster, we address this problem in a language modeling framework. We propose a novel twostage mixture model, which is less sensitive to the number of pseudo feedback documents than an effective existing feedback model. The new model can tolerate a more flexible setting of the number of pseudo feedback documents without the danger of losing much retrieval accuracy.

Citations

234	Document language models, query models, and risk minimization for information retrieval - Lafferty, Zhai - 2001
59	General Probabilistic Framework for Clustering Individuals and Objects - Cadez, Ganffey, et al.
53	Model-based feedback in the kl-divergence retrieval model - Zhai, Lafferty
8	A mixture clustering model for pseudo feedback in information retrieval - Tao, Zhai - 2004