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Return to Session IVa: Formal models - 2
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.
@inproceedings{1009018, author = {Weiguo Fan and Ming Luo and Li Wang and Wensi Xi and Edward A. Fox, 0}, title = {Tuning before feedback: combining ranking discovery and blind feedback for robust retrieval}, booktitle = {SIGIR '04: Proceedings of the 27th annual international conference on Research and development in information retrieval}, year = {2004}, isbn = {1-58113-881-4}, pages = {138--145}, location = {Sheffield, United Kingdom}, doi = {http://doi.acm.org/10.1145/1008992.1009018}, publisher = {ACM Press}, }  ©2005 Association for Computing Machinery |
