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
Matrix computations (3rd ed.)
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
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
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Algorithm 582: The Gibbs-Poole-Stockmeyer and Gibbs-King Algorithms for Reordering Sparse Matrices
ACM Transactions on Mathematical Software (TOMS)
Co-clustering documents and words using bipartite spectral graph partitioning
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Modern Information Retrieval
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
WWW '03 Proceedings of the 12th international conference on World Wide Web
Beyond independent relevance: methods and evaluation metrics for subtopic 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
A generic ranking function discovery framework by genetic programming for information retrieval
Information Processing and Management: an International Journal
Information Retrieval
A study of relevance propagation for web search
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Minimal document set retrieval
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
Learning from labeled and unlabeled data on a directed graph
ICML '05 Proceedings of the 22nd international conference on Machine learning
TREC: Experiment and Evaluation in Information Retrieval (Digital Libraries and Electronic Publishing)
Regularized estimation of mixture models for robust pseudo-relevance feedback
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
Learning to rank networked entities
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
A probabilistic relevance propagation model for hypertext retrieval
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Topic distillation via sub-site retrieval
Information Processing and Management: an International Journal - Special issue: AIRS2005: Information retrieval research in Asia
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
Learning to rank: from pairwise approach to listwise approach
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
Ranking with multiple hyperplanes
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
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Regularizing query-based retrieval scores
Information Retrieval
Query-level loss functions for information retrieval
Information Processing and Management: an International Journal
Using English information in non-English web search
Proceedings of the 2nd ACM workshop on Improving non english web searching
Effective latent space graph-based re-ranking model with global consistency
Proceedings of the Second ACM International Conference on Web Search and Data Mining
A generalized Co-HITS algorithm and its application to bipartite graphs
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning to rank for quantity consensus queries
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Learning Preferences with Hidden Common Cause Relations
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Learning to Rank for Information Retrieval
Foundations and Trends in Information Retrieval
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
A brief survey of computational approaches in social computing
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
LETOR: A benchmark collection for research on learning to rank for information retrieval
Information Retrieval
Fast active exploration for link-based preference learning using Gaussian processes
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
Learning to re-rank web search results with multiple pairwise features
Proceedings of the fourth ACM international conference on Web search and data mining
Topic Distillation with Query-Dependent Link Connections and Page Characteristics
ACM Transactions on the Web (TWEB)
Diversity in ranking via resistive graph centers
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Tag recommendation for georeferenced photos
Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Social Networks
Classification and annotation in social corpora using multiple relations
Proceedings of the 20th ACM international conference on Information and knowledge management
Learning to rank documents using similarity information between objects
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
Movie-DiC: a movie dialogue corpus for research and development
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers - Volume 2
Information Sciences: an International Journal
Effect on generalization of using relational information in list-wise algorithms
ICPCA/SWS'12 Proceedings of the 2012 international conference on Pervasive Computing and the Networked World
Modeling reformulation using query distributions
ACM Transactions on Information Systems (TOIS)
Document Re-ranking Using Partial Social Tagging
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
Exploiting location information for Web search
Computers in Human Behavior
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Learning to rank is a new statistical learning technology on creating a ranking model for sorting objects. The technology has been successfully applied to web search, and is becoming one of the key machineries for building search engines. Existing approaches to learning to rank, however, did not consider the cases in which there exists relationship between the objects to be ranked, despite of the fact that such situations are very common in practice. For example, in web search, given a query certain relationships usually exist among the the retrieved documents, e.g., URL hierarchy, similarity, etc., and sometimes it is necessary to utilize the information in ranking of the documents. This paper addresses the issue and formulates it as a novel learning problem, referred to as, 'learning to rank relational objects'. In the new learning task, the ranking model is defined as a function of not only the contents (features) of objects but also the relations between objects. The paper further focuses on one setting of the learning problem in which the way of using relation information is predetermined. It formalizes the learning task as an optimization problem in the setting. The paper then proposes a new method to perform the optimization task, particularly an implementation based on SVM. Experimental results show that the proposed method outperforms the baseline methods for two ranking tasks (Pseudo Relevance Feedback and Topic Distillation) in web search, indicating that the proposed method can indeed make effective use of relation information and content information in ranking.