The effect multiple query representations on information retrieval system performance
SIGIR '93 Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval
Modern Information Retrieval
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Machine learning for query-document matching in search
Proceedings of the fifth ACM international conference on Web search and data mining
Beyond bag-of-words: machine learning for query-document matching in web search
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
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This paper addresses the problem of dealing with term mismatch in web search using 'blending'. In blending, the input query as well as queries similar to it are used to retrieve documents, the ranking results of documents with respect to the queries are combined to generate a new ranking list. We propose a principled approach to blending, using a kernel method and click-through data. Our approach consists of three elements: a way of calculating query similarity using click-through data, a mixture model for combination of rankings using relevance, query similarity, and document similarity scores, and an algorithm for learning the weights of blending model based on the kernel method. Large scale experiments on web search and enterprise search data sets show that our approach can effectively solve term mismatch problem and significantly outperform the baseline methods of query expansion and heuristic blending.