Automatic text processing
Exponentiated gradient versus gradient descent for linear predictors
Information and Computation
Beyond PageRank: machine learning for static ranking
Proceedings of the 15th international conference on World Wide Web
Cost-effective outbreak detection in networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
An experimental comparison of click position-bias models
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Proceedings of the Second ACM International Conference on Web Search and Data Mining
A dynamic bayesian network click model for web search ranking
Proceedings of the 18th international conference on World wide web
Turning down the noise in the blogosphere
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
A model to estimate intrinsic document relevance from the clickthrough logs of a web search engine
Proceedings of the third ACM international conference on Web search and data mining
SGD-QN: Careful Quasi-Newton Stochastic Gradient Descent
The Journal of Machine Learning Research
Multi-document summarization via budgeted maximization of submodular functions
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
ViewSer: enabling large-scale remote user studies of web search examination and interaction
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Instant foodie: predicting expert ratings from grassroots
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Relevance, diversity and personalization are key issues when presenting content which is apt to pique a user's interest. This is particularly true when presenting an engaging set of news stories. In this paper we propose an efficient algorithm for selecting a small subset of relevant articles from a streaming news corpus. It offers three key pieces of improvement over past work: 1) It is based on a detailed model of a user's viewing behavior which does not require explicit feedback. 2) We use the notion of submodularity to estimate the propensity of interacting with content. This improves over the classical context independent relevance ranking algorithms. Unlike existing methods, we learn the submodular function from the data. 3) We present an efficient online algorithm which can be adapted for personalization, story adaptation, and factorization models. Experiments show that our system yields a significant improvement over a retrieval system deployed in production.