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Li Wang

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Li Wang

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Publications by Li Wang (bibliography)

» 2007 «

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Huang, Sisi, Li, Zhitang and Wang, Li (2007): Mining Attack Correlation Scenarios Based on Multi-agent System. In: Smith, Michael J. and Salvendy, Gavriel (eds.) Symposium on Human Interface 2007 - Part I July 22-27, 2007, Beijing, China. pp. 632-641. Available online

» 2006 «

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Li, Yingjie, Yu, Xueli, Geng, Lili and Wang, Li (2006): Research on Reasoning of the Dynamic Semantic Web Services Composition. In: 2006 IEEE / WIC / ACM International Conference on Web Intelligence WI 2006 18-22 December, 2006, Hong Kong, China. pp. 435-441. Available online

» 2005 «

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Liu, Fang, Wang, Zhiliang, Wang, Li and Meng, Xiuyan (2005): Facial Expression Recognition Using HLAC Features and WPCA. In: Tao, Jianhua, Tan, Tieniu and Picard, Rosalind W. (eds.) ACII 2005 - Affective Computing and Intelligent Interaction, First International Conference October 22-24, 2005, Beijing, China. pp. 88-94. Available online

» 2004 «

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Fan, Weiguo, Luo, Ming, Wang, Li, Xi, Wensi and Fox, Edward A. (2004): Tuning before feedback: combining ranking discovery and blind feedback for robust retrieval. In: Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2004. pp. 138-145. Available online

“Both ranking functions and user queries are very important factors affecting a search engine's performance. Prior research has looked at how to improve ad-hoc retrieval performance for existing queries while tuning the ranking function, or modify and expand user queries using a fixed ranking scheme using blind feedback. However, almost no research has looked at how to combine ranking function tuning and blind feedback together to improve ad-hoc retrieval performance. In this paper, we look at the performance improvement for ad-hoc retrieval from a more integrated point of view by combining the merits of both techniques. In particular, we argue that the ranking function should be tuned first, using user-provided queries, before applying the blind feedback technique. The intuition is that highly-tuned ranking offers more high quality documents at the top of the hit list, thus offers a stronger baseline for blind feedback. We verify this integrated model in a large scale heterogeneous collection and the experimental results show that combining ranking function tuning and blind feedback can improve search performance by almost 30% over the baseline Okapi system.”

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