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
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Query type classification for web document retrieval
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion 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
Adapting ranking SVM to document retrieval
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Ranking with multiple hyperplanes
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
An exploration of proximity measures in information retrieval
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Query dependent ranking using K-nearest neighbor
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
To personalize or not to personalize: modeling queries with variation in user intent
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Upper-bound approximations for dynamic pruning
ACM Transactions on Information Systems (TOIS)
Know your personalization: learning topic level personalization in online services
Proceedings of the 22nd international conference on World Wide Web
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Learning to rank plays an important role in information retrieval. In most of the existing solutions for learning to rank, all the queries with their returned search results are learnt and ranked with a single model. In this paper, we demonstrate that it is highly beneficial to divide queries into multiple groups and conquer search ranking based on query difficulty. To this end, we propose a method which first characterizes a query using a variety of features extracted from user search behavior, such as the click entropy, the query reformulation probability. Next, a classification model is built on these extracted features to assign a score to represent how difficult a query is. Based on this score, our method automatically divides queries into groups, and trains a specific ranking model for each group to conquer search ranking. Experimental results on RankSVM and RankNet with a large-scale evaluation dataset show that the proposed method can achieve significant improvement in the task of web search ranking.