Conference proceeding
Tuning before feedback: combining ranking discovery and blind feedback for robust retrieval
Proceedings of the 27th annual international ACM SIGIR conference on research and development in information retrieval, pp.138-145
SIGIR '04
07/25/2004
DOI: 10.1145/1008992.1009018
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% over the baseline Okapi system.
Details
- Title: Subtitle
- Tuning before feedback: combining ranking discovery and blind feedback for robust retrieval
- Creators
- Weiguo Fan - Virginia TechMing Luo - Virginia TechLi Wang - University of Michigan–Ann ArborWensi Xi - Virginia TechEdward Fox - Virginia Tech
- Resource Type
- Conference proceeding
- Publication Details
- Proceedings of the 27th annual international ACM SIGIR conference on research and development in information retrieval, pp.138-145
- Publisher
- ACM
- Series
- SIGIR '04
- DOI
- 10.1145/1008992.1009018
- Language
- English
- Date published
- 07/25/2004
- Academic Unit
- Business Analytics
- Record Identifier
- 9984380540502771
Metrics
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