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...

• Type: Journal
• Year: 2011
• Where: SIGIR
• Authors: Yuanhua Lv, ChengXiang Zhai, Wan Chen
• Keywords: Information Technology, Optimization Framework, Relevance Feedback, SIGIR 2011, University Of Illinois At Urbana Champaign