TY - GEN
T1 - Optimizing web search using web click-through data
AU - Xue, Gui-Rong
AU - Zeng, Hua-Jun
AU - Chen, Zheng
AU - Yu, Yong
AU - Ma, Wei-Ying
AU - Xi, WenSi
AU - Fan, WeiGuo
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 2004
Y1 - 2004
N2 - 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. Copyright 2004 ACM.
AB - 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. Copyright 2004 ACM.
KW - Click-through Data
KW - Iterative Algorithm
KW - Log Mining
KW - Search Engine
UR - https://www.scopus.com/pages/publications/18744379170
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-18744379170&origin=recordpage
U2 - 10.1145/1031171.1031192
DO - 10.1145/1031171.1031192
M3 - RGC 32 - Refereed conference paper (with host publication)
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 118
EP - 126
BT - CIKM 2004: Proceedings of the Thirteenth ACM Conference on Information and Knowledge Management
PB - Association for Computing Machinery
T2 - CIKM 2004: Proceedings of the Thirteenth ACM Conference on Information and Knowledge Management
Y2 - 8 November 2004 through 13 November 2004
ER -