Conference proceeding
Optimizing web search using web click-through data
Proceedings of the thirteenth ACM international conference on information and knowledge management, pp.118-126
CIKM '04
11/13/2004
DOI: 10.1145/1031171.1031192
Abstract
The performance of web search engines may often deteriorate due to the diversity and noisy information contained within web pages. User click-through data can be used to introduce more accurate description (metadata) for web pages, and to improve the search performance. However, noise and incompleteness, sparseness, and the volatility of web pages and queries are three major challenges for research work on user click-through log mining. In this paper, we propose a novel iterative reinforced algorithm to utilize the user click-through data to improve search performance. The algorithm fully explores the interrelations between queries and web pages, and effectively finds "virtual queries" for web pages and overcomes the challenges discussed above. Experiment results on a large set of MSN click-through log data show a significant improvement on search performance over the naive query log mining algorithm as well as the baseline search engine.
Details
- Title: Subtitle
- Optimizing web search using web click-through data
- Creators
- Gui-Rong Xue - Shanghai Jiao Tong UniversityHua-Jun Zeng - Microsoft Research AsiaZheng Chen - Microsoft Research AsiaYong Yu - Shanghai Jiao Tong UniversityWei-Ying Ma - Microsoft Research AsiaWenSi Xi - Virginia TechWeiGuo Fan - Virginia Tech
- Resource Type
- Conference proceeding
- Publication Details
- Proceedings of the thirteenth ACM international conference on information and knowledge management, pp.118-126
- Publisher
- ACM
- Series
- CIKM '04
- DOI
- 10.1145/1031171.1031192
- Language
- English
- Date published
- 11/13/2004
- Academic Unit
- Business Analytics
- Record Identifier
- 9984380489702771
Metrics
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