A Scalable Bayesian Alternative to Density Estimation with a Bilinear Softmax Function

Ulrich Paquet, Noam Koenigstein, Ole Winther

Research output: Contribution to conferencePaperpeer-review

Abstract

We present a novel, scalable and Bayesian approach to modelling the occurrence of pairs (i, j) drawn from a large vocabulary. Our practical interest is in modelling (user, item) pairs in a recommender system, for which we present state of the art results on Xbox movie viewing data. The 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. The model is a plausible alternative to modelling discrete densities with a bilinear softmax function.
Original languageEnglish
Number of pages8
StatePublished - 2014
Externally publishedYes
EventNEURAL INFORMATION PROCESSING SYSTEMS WORKSHOP, NIPS 2014 - Montreal, Quebec, Canada
Duration: 8 Dec 201413 Dec 2014
http://media.nips.cc/Conferences/2014/NIPS-2014-Workshop-Book.pdf

Workshop

WorkshopNEURAL INFORMATION PROCESSING SYSTEMS WORKSHOP, NIPS 2014
Abbreviated titleNIPS 2014
Country/TerritoryCanada
CityMontreal, Quebec
Period8/12/1413/12/14
Internet address

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