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
Making large-scale support vector machine learning practical
Advances in kernel methods
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
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
Rank aggregation methods for the Web
Proceedings of the 10th international conference on World Wide Web
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
Mining the peanut gallery: opinion extraction and semantic classification of product reviews
WWW '03 Proceedings of the 12th international conference on World Wide Web
Efficient similarity search and classification via rank aggregation
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
An efficient boosting algorithm for combining preferences
The Journal of Machine Learning Research
Discriminative models for information retrieval
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Ranking definitions with supervised learning methods
WWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web
Ranking algorithms for named-entity extraction: boosting and the voted perceptron
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
MailRank: using ranking for spam detection
Proceedings of the 14th 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
New approaches to support vector ordinal regression
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
High accuracy retrieval with multiple nested ranker
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Combating web spam with trustrank
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Learning to rank: from pairwise approach to listwise approach
Proceedings of the 24th international conference on Machine learning
Query-level loss functions for information retrieval
Information Processing and Management: an International Journal
Learning to rank relational objects and its application to web search
Proceedings of the 17th international conference on World Wide Web
Directly optimizing evaluation measures in learning to rank
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
A two-stage text mining model for information filtering
Proceedings of the 17th ACM conference on Information and knowledge management
A Simple Linear Ranking Algorithm Using Query Dependent Intercept Variables
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
RV-SVM: An Efficient Method for Learning Ranking SVM
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Ranking reader emotions using pairwise loss minimization and emotional distribution regression
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Learning to Rank for Information Retrieval
Foundations and Trends in Information Retrieval
Semi-supervised ensemble ranking
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
OrdRank: Learning to Rank with Ordered Multiple Hyperplanes
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Mining Negative Relevance Feedback for Information Filtering
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
A general magnitude-preserving boosting algorithm for search ranking
Proceedings of the 18th ACM conference on Information and knowledge management
Efficient feature weighting methods for ranking
Proceedings of the 18th ACM conference on Information and knowledge management
To divide and conquer search ranking by learning query difficulty
Proceedings of the 18th ACM conference on Information and knowledge management
Enabling multi-level relevance feedback on pubmed by integrating rank learning into DBMS
Proceedings of the third international workshop on Data and text mining in bioinformatics
Mining positive and negative patterns for relevance feature discovery
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
LETOR: A benchmark collection for research on learning to rank for information retrieval
Information Retrieval
Rough sets based reasoning and pattern mining for a two-stage information filtering system
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Selective sampling techniques for feedback-based data retrieval
Data Mining and Knowledge Discovery
Learning to rank with document ranks and scores
Knowledge-Based Systems
Transductive learning to rank using association rules
Expert Systems with Applications: An International Journal
A pattern mining approach for information filtering systems
Information Retrieval
Pattern mining for a two-stage information filtering system
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part I
Learning to rank with nonlinear monotonic ensemble
MCS'11 Proceedings of the 10th international conference on Multiple classifier systems
Search behavior-driven training for result re-ranking
TPDL'11 Proceedings of the 15th international conference on Theory and practice of digital libraries: research and advanced technology for digital libraries
Proceedings of the 20th ACM international conference on Information and knowledge management
Modeling personalized email prioritization: classification-based and regression-based approaches
Proceedings of the 20th ACM international conference on Information and knowledge management
A two-stage decision model for information filtering
Decision Support Systems
An efficient method for learning nonlinear ranking SVM functions
Information Sciences: an International Journal
Learning conditional preference network from noisy samples using hypothesis testing
Knowledge-Based Systems
Relative forest for attribute prediction
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
Robust ordinal regression in preference learning and ranking
Machine Learning
Text mining in negative relevance feedback
Web Intelligence and Agent Systems
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The central problem for many applications in Information Retrieval is ranking and learning to rank is considered as a promising approach for addressing the issue. Ranking SVM, for example, is a state-of-the-art method for learning to rank and has been empirically demonstrated to be effective. In this paper, we study the issue of learning to rank, particularly the approach of using SVM techniques to perform the task. We point out that although Ranking SVM is advantageous, it still has shortcomings. Ranking SVM employs a single hyperplane in the feature space as the model for ranking, which is too simple to tackle complex ranking problems. Furthermore, the training of Ranking SVM is also computationally costly. In this paper, we look at an alternative approach to Ranking SVM, which we call "Multiple Hyperplane Ranker" (MHR), and make comparisons between the two approaches. MHR takes the divide-and-conquer strategy. It employs multiple hyperplanes to rank instances and finally aggregates the ranking results given by the hyperplanes. MHR contains Ranking SVM as a special case, and MHR can overcome the shortcomings which Ranking SVM suffers from. Experimental results on two information retrieval datasets show that MHR can outperform Ranking SVM in ranking.