Modern Information Retrieval
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Web metasearch: rank vs. score based rank aggregation methods
Proceedings of the 2003 ACM symposium on Applied computing
An efficient boosting algorithm for combining preferences
The Journal of Machine Learning Research
Optimizing web search using web click-through data
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Learning to rank using gradient descent
ICML '05 Proceedings of the 22nd international conference on Machine learning
Improving web search ranking by incorporating user behavior information
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Adapting ranking SVM to document retrieval
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
| Hi-index | 0.00 |
Many different ranking algorithms based on content and context have been used in web search engines to find pages based on a user query. Furthermore, to achieve better performance some new solutions combine different algorithms. In this paper we use simulated click-through data to learn how to combine many content and context features of web pages. This method is simple and practical to use with actual click-through data in a live search engine. The proposed approach is evaluated using the LETOR benchmark and we found it is competitive to Ranking SVM based on user judgments.