On the limited memory BFGS method for large scale optimization
Mathematical Programming: Series A and B
Algorithms for scalable synchronization on shared-memory multiprocessors
ACM Transactions on Computer Systems (TOCS)
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Methods and metrics for cold-start recommendations
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
The Journal of Machine Learning Research
Unifying collaborative and content-based filtering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Naïve filterbots for robust cold-start recommendations
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Mixtures of hierarchical topics with Pachinko allocation
Proceedings of the 24th international conference on Machine learning
Bayesian probabilistic matrix factorization using Markov chain Monte Carlo
Proceedings of the 25th international conference on Machine learning
Regression-based latent factor models
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
A New Approach to Collaborative Filtering: Operator Estimation with Spectral Regularization
The Journal of Machine Learning Research
An architecture for parallel topic models
Proceedings of the VLDB Endowment
Modeling the dynamics of composite social networks
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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Content personalization is a key tool in creating attractive websites. Synergies can be obtained by integrating personalization between several Internet properties. In this paper we propose a hierarchical Bayesian model to address these issues. Our model allows the integration of multiple properties without changing the overall structure, which makes it easily extensible across large Internet portals. It relies at its lowest level on Latent Dirichlet Allocation, while making use of latent side features for cross-property integration. We demonstrate the efficiency of our approach by analyzing data from several properties of a major Internet portal.