OHSUMED: an interactive retrieval evaluation and new large test collection for research
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
IR evaluation methods for retrieving highly relevant documents
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Modern Information Retrieval
An efficient boosting algorithm for combining preferences
The Journal of Machine Learning Research
Learning to rank using gradient descent
ICML '05 Proceedings of the 22nd international conference on Machine learning
Learning to rank: from pairwise approach to listwise approach
Proceedings of the 24th international conference on Machine learning
Ranking with multiple hyperplanes
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
AdaRank: a boosting algorithm for information retrieval
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Learning to rank relational objects and its application to web search
Proceedings of the 17th international conference on World Wide Web
Listwise approach to learning to rank: theory and algorithm
Proceedings of the 25th international conference on Machine learning
Query dependent ranking using K-nearest neighbor
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Learning to rank at query-time using association rules
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
| Hi-index | 0.00 |
Ranking is a central problem for information retrieval systems, because the performance of an information retrieval system is mainly evaluated by the effectiveness of its ranking results. Learning to rank has received much attention in recent years due to its importance in information retrieval. This paper focuses on learning to rank in document retrieval and presents a ranking model named OrdRank that ranks documents with ordered multiple hyperplanes. Comparison of OrdRank with other state-of-the-art ranking techniques is conducted and several evaluation criteria are employed to evaluate its performance. Experimental results on the OHSUMED dataset show that OrdRank outperforms other methods, both in terms of quality of ranking results and efficiency.