A general language model for information retrieval
Proceedings of the eighth international conference on Information and knowledge management
A vector space model for automatic indexing
Communications of the ACM
Document language models, query models, and risk minimization for information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Modern Information Retrieval
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
Computational Statistics & Data Analysis - Nonlinear methods and data mining
An Efficient Boosting Algorithm for Combining Preferences
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Accurately interpreting clickthrough data as implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Learning to rank using gradient descent
ICML '05 Proceedings of the 22nd international conference on Machine learning
A support vector method for multivariate performance measures
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
Learning to rank: from pairwise approach to listwise approach
Proceedings of the 24th international conference on Machine learning
Magnitude-preserving ranking algorithms
Proceedings of the 24th international conference on Machine learning
A support vector method for optimizing average precision
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
A regression framework for learning ranking functions using relative relevance judgments
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
FRank: a ranking method with fidelity loss
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
Active exploration for learning rankings from clickthrough data
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the Second ACM International Conference on Web Search and Data Mining
Learning to Rank for Information Retrieval
Foundations and Trends in Information Retrieval
Post-rank reordering: resolving preference misalignments between search engines and end users
Proceedings of the 18th ACM conference on Information and knowledge management
A Boosting Approach for Learning to Rank Using SVD with Partially Labeled Data
AIRS '09 Proceedings of the 5th Asia Information Retrieval Symposium on Information Retrieval Technology
A brief survey of computational approaches in social computing
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Towards mobile intelligence: Learning from GPS history data for collaborative recommendation
Artificial Intelligence
Social temporal collaborative ranking for context aware movie recommendation
ACM Transactions on Intelligent Systems and Technology (TIST) - Special section on twitter and microblogging services, social recommender systems, and CAMRa2010: Movie recommendation in context
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Designing effective ranking functions is a core problem for information retrieval and Web search since the ranking functions directly impact the relevance of the search results. The problem has been the focus of much of the research at the intersection of Web search and machine learning, and learning ranking functions from preference data in particular has recently attracted much interest. The objective of this paper is to empirically examine several objective functions that can be used for learning ranking functions from preference data. Specifically, we investigate the roles of ties in the learning process. By ties, we mean preference judgments that two documents have equal degree of relevance with respect to a query. This type of data has largely been ignored or not properly modeled in the past. In this paper, we analyze the properties of ties and develop novel learning frameworks which combine ties and preference data using statistical paired comparison models to improve the performance of learned ranking functions. The resulting optimization problems explicitly incorporating ties and preference data are solved using gradient boosting methods. Experimental studies are conducted using three publicly available data sets which demonstrate the effectiveness of the proposed new methods.