%0 Conference Paper %1 Fan2004sigir %A Fan, Weiguo %A Luo, Ming %A Wang, Li %A Xi, Wensi %A Fox, Edward A. %B the Proceedings of the 27th Annual International ACM SIGIR Conference %C U.K. %D 2004 %I ACM %K algorithms, blind discovery, engine, feedback function genetic information intelligent programming, ranking retrieval, search %T Tuning before feedback: combining ranking function discovery and blind feedback for robust retrieval %U http://filebox.vt.edu/users/wfan/paper/ARRANGER/p52-Fan.pdf %X 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 percent over the baseline Okapi system.

@inproceedings{Fan2004sigir, abstract = {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 percent over the baseline Okapi system.}, added-at = {2008-06-19T17:35:00.000+0200}, address = {U.K.}, author = {Fan, Weiguo and Luo, Ming and Wang, Li and Xi, Wensi and Fox, Edward A.}, biburl = {https://www.bibsonomy.org/bibtex/295fc790b5be2c210ab5192f35e605f3f/brazovayeye}, booktitle = {the Proceedings of the 27th Annual International ACM SIGIR Conference}, interhash = {ac75959489f14ed9a457cc2829b44a39}, intrahash = {95fc790b5be2c210ab5192f35e605f3f}, keywords = {algorithms, blind discovery, engine, feedback function genetic information intelligent programming, ranking retrieval, search}, publisher = {ACM}, timestamp = {2008-06-19T17:39:24.000+0200}, title = {Tuning before feedback: combining ranking function discovery and blind feedback for robust retrieval}, url = {http://filebox.vt.edu/users/wfan/paper/ARRANGER/p52-Fan.pdf}, year = 2004 }