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SIGIR
2011
ACM

A boosting approach to improving pseudo-relevance feedback

7 years 9 months ago
A boosting approach to improving pseudo-relevance feedback
Pseudo-relevance feedback has proven effective for improving the average retrieval performance. Unfortunately, many experiments have shown that although pseudo-relevance feedback helps many queries, it also often hurts many other queries, limiting its usefulness in real retrieval applications. Thus an important, yet difficult challenge is to improve the overall effectiveness of pseudo-relevance feedback without sacrificing the performance of individual queries too much. In this paper, we propose a novel learning algorithm, FeedbackBoost, based on the boosting framework to improve pseudo-relevance feedback through optimizing the combination of a set of basis feedback algorithms using a loss function defined to directly measure both robustness and effectiveness. FeedbackBoost can potentially accommodate many basis feedback methods as features in the model, making the proposed method a general optimization framework for pseudo-relevance feedback. As an application, we apply Feedback...
Yuanhua Lv, ChengXiang Zhai, Wan Chen
Added 17 Sep 2011
Updated 17 Sep 2011
Type Journal
Year 2011
Where SIGIR
Authors Yuanhua Lv, ChengXiang Zhai, Wan Chen
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