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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 two-stage 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.
@inproceedings{1009083, author = {Tao Tao and ChengXiang Zhai}, title = {A two-stage mixture model for pseudo feedback}, booktitle = {SIGIR '04: Proceedings of the 27th annual international conference on Research and development in information retrieval}, year = {2004}, isbn = {1-58113-881-4}, pages = {486--487}, location = {Sheffield, United Kingdom}, doi = {http://doi.acm.org/10.1145/1008992.1009083}, publisher = {ACM Press}, }  ©2005 Association for Computing Machinery |
