Xbox movies recommendations: Variational bayes matrix factorization with embedded feature selection

Noam Koenigstein, Ulrich Paquet

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We present a matrix factorization model inspired by challenges we encountered while working on the Xbox movies recommendation system. The item catalog in a recommender system is typically equipped with meta-data features in the form of labels. However, only part of these features are informative or useful with regard to collaborative filtering. By incorporating a novel sparsity prior on feature parameters, the model automatically discerns and utilizes informative features while simultaneously pruning non-informative features. The model is designed for binary feedback, which is common in many real-world systems where numeric rating data is scarce or non-existent. However, the overall framework is applicable to any likelihood function. Model parameters are estimated with a Variational Bayes inference algorithm, which is robust to over-fitting and does not require crossvalidation and fine tuning of regularization coefficients. The efficacy of our method is illustrated on a sample from the Xbox movies dataset as well as on the publicly available MovieLens dataset. In both cases, the proposed solution provides superior predictive accuracy, especially for long-tail items. We then demonstrate the feature selection capabilities and compare against the common case of simple Gaussian priors. Finally, we show that even without features, our model performs better than a baseline model trained with the popular stochastic gradient descent approach.

Original languageEnglish
Title of host publicationRecSys 2013 - Proceedings of the 7th ACM Conference on Recommender Systems
Pages129-136
Number of pages8
DOIs
StatePublished - 2013
Externally publishedYes
Event7th ACM Conference on Recommender Systems, RecSys 2013 - Hong Kong, China
Duration: 12 Oct 201316 Oct 2013

Publication series

NameRecSys 2013 - Proceedings of the 7th ACM Conference on Recommender Systems

Conference

Conference7th ACM Conference on Recommender Systems, RecSys 2013
Country/TerritoryChina
CityHong Kong
Period12/10/1316/10/13

Keywords

  • Feature selection
  • Recommender system

All Science Journal Classification (ASJC) codes

  • Software

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