Proceedings of the 11th international conference on World Wide Web
Scaling personalized web search
WWW '03 Proceedings of the 12th international conference on World Wide Web
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
An efficient boosting algorithm for combining preferences
The Journal of Machine Learning Research
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Semi-Supervised Learning on Riemannian Manifolds
Machine Learning
Query chains: learning to rank from implicit feedback
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Learning to rank using gradient descent
ICML '05 Proceedings of the 22nd international conference on Machine learning
Automatic identification of user interest for personalized search
Proceedings of the 15th international conference on World Wide Web
ICML '06 Proceedings of the 23rd international conference on Machine learning
A boosting algorithm for learning bipartite ranking functions with partially labeled data
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Learning to rank with partially-labeled data
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Introduction to Information Retrieval
Introduction to Information Retrieval
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In this paper we present a semi-supervised learning method for a problem of learning to rank where we exploit Markov random walks and graph regularization in order to incorporate not only "labeled" web pages but also plenty of "un-labeled" web pages (click logs of which are not given) into learning a ranking function. In order to cope with scalability which existing semi-supervised learning methods suffer from, we develop a scalable and incremental method for semi-supervised learning to rank. In the graph regularization framework, we first determine features which well reflects data manifold and then make use of them to train a linear ranking function. We introduce a matrix-fee technique where we compute the eigenvectors of a huge similarity matrix without constructing the matrix itself. Then we present an incremental algorithm to learn a linear ranking function using features determined by projecting data onto the eigenvectors of the similarity matrix, which can be applied to a task of web-scale ranking. We evaluate our method on Live Search query log, showing that search performance is much improved when Live Search yields unsatisfactory search results.