A 2-Stage Ranking Method to Merge Multiple Search Results
Date Issued
2009
Date
2009
Author(s)
Lin, You-Lin
Abstract
Metasearch is the problem that discusses how to combine the results of multiple independent search algorithms into one single result list and tries to improve the effectiveness of the retrieval. We propose a novel 2-stage ranking method to do this by applying the technology of machine learning. The 2-stage ranking method aims to use the concept of classification to solve the metasearch problem. In the first stage, we try to label each document in the search result with relevance or irrelevance by classification, where we discuss the differences between general classification and cost-sensitive classification in our algorithm. Once we have labeled all of the documents in the search result, in stage 2, we can use this information to produce the final ranking result by using linear combination. The 2-stage ranking method performs well on NTCIR4 English-English IR data. The experiment result shows that our method outperforms the existed metasearch algorithms and gives a significant improvement.
Subjects
metaseach
learning to rank
search result merging
Type
thesis
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