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arXiv · 1409.2824

Scalable Bayesian Modelling of Paired Symbols

Ulrich Paquet·Noam Koenigstein·Ole Winther
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Abstract

We present a novel, scalable and Bayesian approach to modelling the occurrence of pairs of symbols (i,j) drawn from a large vocabulary. Observed pairs are assumed to be generated by a simple popularity based selection process followed by censoring using a preference function. By basing inference on the well-founded principle of variational bounding, and using new site-independent bounds, we show how a scalable inference procedure can be obtained for large data sets. State of the art results are presented on real-world movie viewing data.

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Ulrich Paquet, Noam Koenigstein, Ole Winther. 2014-09-10. Scalable Bayesian Modelling of Paired Symbols. https://arxiv.org/abs/1409.2824

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