A Learning-Based Term-Weighting Approach for Information Retrieval

One of the core components in information retrieval(IR) is the document-term-weighting scheme. In this paper, we will propose a novel learning-based term-weighting approach to improve the retrieval performance of vector space model in homogeneous collections. We first introduce a simple learning system to weighting the index terms of documents. Then, we deduce a formal computational approach according to some theories of matrix computation and statistical inference. Our experiments on 8 collections will show that our approach outperforms classic tfidf weighting, about 20%∼45%.

Authors

• Guang Can Liu Yong Yu

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