A language modeling approach to information retrieval
Proceedings of the 21st 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
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Learning the Kernel Matrix with Semi-Definite Programming
ICML '02 Proceedings of the Nineteenth 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
Rademacher and gaussian complexities: risk bounds and structural results
The Journal of Machine Learning Research
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
A study of smoothing methods for language models applied to information retrieval
ACM Transactions on Information Systems (TOIS)
Boosting margin based distance functions for clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Multiple kernel learning, conic duality, and the SMO algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Learning the Kernel with Hyperkernels
The Journal of Machine Learning Research
Learning the Kernel Function via Regularization
The Journal of Machine Learning Research
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 Distance Metrics with Contextual Constraints for Image Retrieval
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis
The Journal of Machine Learning Research
Learning to rank: from pairwise approach to listwise approach
Proceedings of the 24th international conference on Machine learning
Information-theoretic metric learning
Proceedings of the 24th international conference on Machine learning
Random walks on the click graph
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
Extracting semantic relations from query logs
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
A Discriminative Kernel-Based Approach to Rank Images from Text Queries
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning latent semantic relations from clickthrough data for query suggestion
Proceedings of the 17th ACM conference on Information and knowledge management
More generality in efficient multiple kernel learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Invited talk: Can learning kernels help performance?
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Distance Metric Learning for Large Margin Nearest Neighbor Classification
The Journal of Machine Learning Research
A New Approach to Collaborative Filtering: Operator Estimation with Spectral Regularization
The Journal of Machine Learning Research
An efficient algorithm for local distance metric learning
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Learning to Rank for Information Retrieval
Foundations and Trends in Information Retrieval
Proceedings of the 18th ACM conference on Information and knowledge management
Clickthrough-based latent semantic models for web search
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Learning a Robust Relevance Model for Search Using Kernel Methods
The Journal of Machine Learning Research
Stability and generalization of bipartite ranking algorithms
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Margin-Based ranking meets boosting in the middle
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Overview and recent advances in partial least squares
SLSFS'05 Proceedings of the 2005 international conference on Subspace, Latent Structure and Feature Selection
A Unifying Discussion of Correlation Analysis for Complex Random Vectors
IEEE Transactions on Signal Processing
SVDFeature: a toolkit for feature-based collaborative filtering
The Journal of Machine Learning Research
Hi-index | 0.00 |
The task of matching data from two heterogeneous domains naturally arises in various areas such as web search, collaborative filtering, and drug design. In web search, existing work has designed relevance models to match queries and documents by exploiting either user clicks or content of queries and documents. To the best of our knowledge, however, there has been little work on principled approaches to leveraging both clicks and content to learn a matching model for search. In this paper, we propose a framework for learning to match heterogeneous objects. The framework learns two linear mappings for two objects respectively, and matches them via the dot product of their images after mapping. Moreover, when different regularizations are enforced, the framework renders a rich family of matching models. With orthonormal constraints on mapping functions, the framework subsumes Partial Least Squares (PLS) as a special case. Alternatively, with a l1+l2 regularization, we obtain a new model called Regularized Mapping to Latent Structures (RMLS). RMLS enjoys many advantages over PLS, including lower time complexity and easy parallelization. To further understand the matching framework, we conduct generalization analysis and apply the result to both PLS and RMLS. We apply the framework to web search and implement both PLS and RMLS using a click-through bipartite with metadata representing features of queries and documents. We test the efficacy and scalability of RMLS and PLS on large scale web search problems. The results show that both PLS and RMLS can significantly outperform baseline methods, while RMLS substantially speeds up the learning process.