Abstract
Relevance feedback, which uses the terms in relevant documents to enrich the user’s initial query, is an effective method for improving retrieval performance. An associated key research problem is the following: Which documents to present to the user so that the user’s feedback on the documents can significantly impact relevance feedback performance. This paper views this as an active learning problem and proposes a new algorithm which can efficiently maximize the learning benefits of relevance feedback. This algorithm chooses a set of feedback documents based on relevancy, document diversity and document density. Experimental results show a statistically significant and appreciable improvement in the performance of our new approach over the existing active feedback methods.
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References
Harman, D.: Relevance feedback revisited. In: Proceedings of the 15th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1–10. ACM Press, New York (1992)
Salton, G., Buckley, C.: Improving retrieval performance by relevance feedback. Journal of the American Society for Information Science 41(4), 133–168 (1990)
Shen, X., Zhai, C.: Active feedback in ad hoc information retrieval. In: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, March 2005, pp. 55–66. ACM Press, New York (2005)
Cohn, D.A., Ghahramani, Z., Jordan, M.I.: Active learning with statistical models. In: Advances in Neural Information Processing Systems, vol. 7, pp. 705–712. MIT Press, Cambridge (1995)
Freund, Y., et al.: Selective sampling using the query by committee algorithm. Machine Learning 28(2-3), 133–168 (1997)
Tong, S., Koller, D.: Support vector machine active learning with applications to text classification. In: Proceedings of 17th International Conference on Machine Learning, pp. 999–1006 (2000)
McCallum, A., Nigam, K.: Employing EM and pool-based active learning for text classification. In: Proceedings of the Fifteenth International Conference on Machine Learning, pp. 350–358 (1998)
Brinker, K.: Incorporating diversity in active learning with support vector machines. In: Proceedings of the Twentieth International Conference on Machine Learning , pp. 59–66 (2003)
Zhang, Y., Xu, W., Callan, J.: Exploration and exploitation in adaptive filtering based on bayesian active learning. In: Proceedings of 20th International Conf. on Machine Learning, pp. 896–903 (2003)
Lafferty, J., Zhai, C.: Document language models, query models, and risk minimization for information retrieval. In: Research and Development in Information Retrieval, pp. 111–119 (2001)
Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information Theory 37(1), 145–151 (1991)
Carbonell, J.G., Goldstein, J.: The use of MMR, diversity-based reranking for reordering documents and producing summaries. In: Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 335–336. ACM Press, New York (1998)
Zhai, C., Lafferty, J.: Model-based feedback in the language modeling approach to information retrieval. In: Proceedings of the Tenth ACM International Conference on Information and Knowledge Management, pp. 403–410. ACM Press, New York (2001)
(The lemur toolkit), http://www.lemurproject.org
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Xu, Z., Akella, R., Zhang, Y. (2007). Incorporating Diversity and Density in Active Learning for Relevance Feedback. In: Amati, G., Carpineto, C., Romano, G. (eds) Advances in Information Retrieval. ECIR 2007. Lecture Notes in Computer Science, vol 4425. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71496-5_24
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DOI: https://doi.org/10.1007/978-3-540-71496-5_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71494-1
Online ISBN: 978-3-540-71496-5
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