Some simple effective approximations to the 2-Poisson model for probabilistic weighted retrieval
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
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Machine Learning - Special issue on inductive transfer
Machine Learning - Special issue on inductive transfer
Learning to learn
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
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Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Modern Information Retrieval
Machine Learning - Special issue on inductive transfer
A perspective view and survey of meta-learning
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Implicit feedback for inferring user preference: a bibliography
ACM SIGIR Forum
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Machine Learning
Evaluating implicit measures to improve web search
ACM Transactions on Information Systems (TOIS)
Combining eye movements and collaborative filtering for proactive information retrieval
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Accurately interpreting clickthrough data as implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
User performance versus precision measures for simple search tasks
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Learning Object Shape: From Drawings to Images
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data
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Inferring word relevance from eye-movements of readers
Proceedings of the 16th international conference on Intelligent user interfaces
Probabilistic proactive timeline browser
ICANN'11 Proceedings of the 21st international conference on Artificial neural networks - Volume Part II
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In the absence of explicit queries, an alternative is to try to infer users' interests from implicit feedback signals, such as clickstreams or eye tracking. The interests, formulated as an implicit query, can then be used in further searches. We formulate this task as a probabilistic model, which can be interpreted as a kind of transfer or meta-learning. The probabilistic model is demonstrated to outperform an earlier kernel-based method in a small-scale information retrieval task.