A Classification EM algorithm for clustering and two stochastic versions
Computational Statistics & Data Analysis - Special issue on optimization techniques in statistics
Bayesian forecasting and dynamic models (2nd ed.)
Bayesian forecasting and dynamic models (2nd ed.)
Open user profiles for adaptive news systems: help or harm?
Proceedings of the 16th international conference on World Wide Web
Google news personalization: scalable online collaborative filtering
Proceedings of the 16th international conference on World Wide Web
Predicting clicks: estimating the click-through rate for new ads
Proceedings of the 16th international conference on World Wide Web
Trust region Newton methods for large-scale logistic regression
Proceedings of the 24th international conference on Machine learning
Modeling relationships at multiple scales to improve accuracy of large recommender systems
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Contextual advertising by combining relevance with click feedback
Proceedings of the 17th international conference on World Wide Web
Computational advertising and recommender systems
Proceedings of the 2008 ACM conference on Recommender systems
Personalized recommendation on dynamic content using predictive bilinear models
Proceedings of the 18th international conference on World wide web
Regression-based latent factor models
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Large-scale behavioral targeting
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Collaborative filtering with temporal dynamics
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
A spatio-temporal approach to collaborative filtering
Proceedings of the third ACM conference on Recommender systems
Personalized search on the world wide web
The adaptive web
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Incremental Collaborative Filtering recommender based on Regularized Matrix Factorization
Knowledge-Based Systems
Targeting converters for new campaigns through factor models
Proceedings of the 21st international conference on World Wide Web
Finding trending local topics in search queries for personalization of a recommendation system
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning binary codes for collaborative filtering
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Real-time top-n recommendation in social streams
Proceedings of the sixth ACM conference on Recommender systems
An Online Learning Framework for Refining Recency Search Results with User Click Feedback
ACM Transactions on Information Systems (TOIS)
Recommendation in Online Health Communities
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Content recommendation on web portals
Communications of the ACM
Timespent based models for predicting user retention
Proceedings of the 22nd international conference on World Wide Web
Boosting the K-Nearest-Neighborhood based incremental collaborative filtering
Knowledge-Based Systems
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Recommender problems with large and dynamic item pools are ubiquitous in web applications like content optimization, online advertising and web search. Despite the availability of rich item meta-data, excess heterogeneity at the item level often requires inclusion of item-specific "factors" (or weights) in the model. However, since estimating item factors is computationally intensive, it poses a challenge for time-sensitive recommender problems where it is important to rapidly learn factors for new items (e.g., news articles, event updates, tweets) in an online fashion. In this paper, we propose a novel method called FOBFM (Fast Online Bilinear Factor Model) to learn item-specific factors quickly through online regression. The online regression for each item can be performed independently and hence the procedure is fast, scalable and easily parallelizable. However, the convergence of these independent regressions can be slow due to high dimensionality. The central idea of our approach is to use a large amount of historical data to initialize the online models based on offline features and learn linear projections that can effectively reduce the dimensionality. We estimate the rank of our linear projections by taking recourse to online model selection based on optimizing predictive likelihood. Through extensive experiments, we show that our method significantly and uniformly outperforms other competitive methods and obtains relative lifts that are in the range of 10-15% in terms of predictive log-likelihood, 200-300% for a rank correlation metric on a proprietary My Yahoo! dataset; it obtains 9% reduction in root mean squared error over the previously best method on a benchmark MovieLens dataset using a time-based train/test data split.