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A Boosting Approach to Improving Pseudo-Relevance Feedback Yuanhua Lv , ChengXiang Zhai and Wan Chen 2011 SIGIR. outline. Introduction Proposed method Experiments Conclusions . Introduction.

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  1. A Boosting Approach to Improving Pseudo-Relevance FeedbackYuanhuaLv, ChengXiangZhaiand Wan Chen2011 SIGIR

  2. outline • Introduction • Proposed method • Experiments • Conclusions

  3. Introduction • The basic idea of pseudo feedback is to assume a certain number of top-ranked documents from an initial retrieval run to be relevant and extract useful information from these feedback documents to improve the original query. • Although traditional pseudo feedback techniques generally improve retrieval performance (e.g., AP) on average, they are not robust in the sense that they tend to help some queries, but hurt other queries. • Based on the boosting framework to improve pseudo-relevance feedback through combining a set of basis feedback algorithms optimally using a loss function defined to directly measure both robustness and effectiveness, whichhas not been achieved in any previous work on pseudo feedback.

  4. Proposed method • qiand a document collection C, • a retrieval function F returns a ranked list of m documents • a feedback model φt(qi, d, n,C), or φt(qi) • φt(qi) is essentially an expanded representationof the original query qi, • a performance measure E • the relevance judgments set J(qi) for query qi,.

  5. Proposed method

  6. Proposed method • APPLICATION OF FEEDBACKBOOST TO LANGUAGE MODELS • Relevance Model • Mixture Model

  7. Experiments

  8. Experiments

  9. Conclusions • We propose a novel learning algorithm, FeedbackBoost, based on the boosting framework to improve pseudo feedback. A major contribution of our work is to optimize pseudo feedback based on a novel loss function that directly measures both robustness and effectiveness. • The experiment results show that the proposed Feedback- boost algorithm can improve average precision effectively and meanwhile reduce the number and magnitude of feedback failures dramatically as compared to two representative pseudo feedback methods based on language models, the mixture model and the relevance model.

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