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Query expansion using local and global document analysis
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Improving retrieval performance by relevance feedback
Readings in information retrieval
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Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
A language modeling approach to information retrieval
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
IR evaluation methods for retrieving highly relevant documents
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ACM Transactions on Information Systems (TOIS)
Modern Information Retrieval
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The Journal of Machine Learning Research
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This paper points out that many search relevance models in information retrieval, such as the Vector Space Model, BM25 and Language Models for Information Retrieval, can be viewed as a similarity function between pairs of objects of different types, referred to as an S-function. An S-function is specifically defined as the dot product between the images of two objects in a Hilbert space mapped from two different input spaces. One advantage of taking this view is that one can take a unified and principled approach to address the issues with regard to search relevance. The paper then proposes employing a kernel method to learn a robust relevance model as an S-function, which can effectively deal with the term mismatch problem, one of the biggest challenges in search. The kernel method exploits a positive semi-definite kernel referred to as an S-kernel. The paper shows that when using an S-kernel the model learned by the kernel method is guaranteed to be an S-function. The paper then gives more general principles for constructing S-kernels. A specific implementation of the kernel method is proposed using the Ranking SVM techniques and click-through data. The proposed approach is employed to learn a relevance model as an extension of BM25, referred to as Robust BM25. Experimental results on web search and enterprise search data show that Robust BM25 significantly outperforms baseline methods and can successfully tackle the term mismatch problem.