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• W628035 abstract "Discrete mixed membership modeling and continuous latent factor modeling (also known as matrix factorization) are two popular, complementary approaches to dyadic data analysis. In this work, we develop a fully Bayesian framework for integrating the two approaches into unified Mixed Membership Matrix Factorization (M3F) models. We introduce two M3F models, derive Gibbs sampling inference procedures, and validate our methods on the EachMovie, MovieLens, and Netflix Prize collaborative filtering datasets. We find that, even when fitting fewer parameters, the M3F models outperform state-of-the-art latent factor approaches on all benchmarks, yielding the greatest gains in accuracy on sparsely-rated, high-variance items." @default.
• W628035 created "2016-06-24" @default.
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• W628035 date "2014-11-06" @default.
• W628035 modified "2023-09-27" @default.
• W628035 title "Block-LDA: Jointly Modeling Entity-Annotated Text and Entity-Entity Links" @default.
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• W628035 doi "https://doi.org/10.1201/b17520-22" @default.
• W628035 hasPublicationYear "2014" @default.
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