A family of algorithms for approximate bayesian inference
A family of algorithms for approximate bayesian inference
An efficient boosting algorithm for combining preferences
The Journal of Machine Learning Research
Active Sampling for Class Probability Estimation and Ranking
Machine Learning
Active learning of label ranking functions
ICML '04 Proceedings of the twenty-first international conference on Machine learning
SVM selective sampling for ranking with application to data retrieval
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
Preference learning with Gaussian processes
ICML '05 Proceedings of the 22nd international conference on Machine learning
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Evaluating the accuracy of implicit feedback from clicks and query reformulations in Web search
ACM Transactions on Information Systems (TOIS)
Active exploration for learning rankings from clickthrough data
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Learning to rank relational objects and its application to web search
Proceedings of the 17th international conference on World Wide Web
Learning to rank with SoftRank and Gaussian processes
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Introduction to Information Retrieval
Introduction to Information Retrieval
Label ranking by learning pairwise preferences
Artificial Intelligence
Active Sampling for Rank Learning via Optimizing the Area under the ROC Curve
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
Interactively optimizing information retrieval systems as a dueling bandits problem
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
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
LETOR: A benchmark collection for research on learning to rank for information retrieval
Information Retrieval
Balancing exploration and exploitation in learning to rank online
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
Efficiently learning the preferences of people
Machine Learning
Exploration in relational domains for model-based reinforcement learning
The Journal of Machine Learning Research
Exploratory and interactive daily deals recommendation
Proceedings of the 7th ACM conference on Recommender systems
Learning community-based preferences via dirichlet process mixtures of Gaussian processes
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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In preference learning, the algorithm observes pairwise relative judgments (preference) between items as training data for learning an ordering of all items. This is an important learning problem for applications where absolute feedback is difficult to elicit, but pairwise judgments are readily available (e.g., via implicit feedback [13]). While it was already shown that active learning can effectively reduce the number of training pairs needed, the most successful existing algorithms cannot generalize over items or queries. Considering web search as an example, they would need to learn a separate relevance score for each document-query pair from scratch. To overcome this inefficiency, we propose a link-based active preference learning method based on Gaussian Processes (GPs) that incorporates dependency information from both feature-vector representations as well as relations. Specifically, to meet the requirement on computational efficiency of active exploration, we introduce a novel incremental update method that scales as well as the nongeneralizing models. The proposed algorithm is evaluated on datasets for information retrieval, showing that it learns substantially faster than algorithms that cannot model dependencies.