@inproceedings{3eda8ed668ad49479ab89fa7e43ca1d3,
title = "A two-stage mixture model for pseudo feedback",
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 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.",
keywords = "Information retrieval, Mixture model, Pseudo feedback",
author = "Tao Tao and Chengxiang Zhai",
year = "2004",
doi = "10.1145/1008992.1009083",
language = "English (US)",
isbn = "1581138814",
series = "Proceedings of Sheffield SIGIR - Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval",
publisher = "Association for Computing Machinery",
pages = "486--487",
booktitle = "Proceedings of Sheffield SIGIR - Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval",
address = "United States",
note = "Proceedings of Sheffield SIGIR - Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval ; Conference date: 25-07-2004 Through 29-07-2004",
}