One-class Collaborative Filtering with Random Graphs: Annotated Version
This addresses a key challenge in recommendation systems for platforms like Xbox Live, though it appears incremental by building on existing collaborative filtering methods.
The paper tackles the problem of interpreting latent signals from missing data in one-class collaborative filtering by proposing a novel Bayesian generative model that distinguishes between user dislike and non-consideration of items. It demonstrates large-scale distributed learning on real-world data, showing competitive performance against a state-of-the-art baseline.
The bane of one-class collaborative filtering is interpreting and modelling the latent signal from the missing class. In this paper we present a novel Bayesian generative model for implicit collaborative filtering. It forms a core component of the Xbox Live architecture, and unlike previous approaches, delineates the odds of a user disliking an item from simply not considering it. The latent signal is treated as an unobserved random graph connecting users with items they might have encountered. We demonstrate how large-scale distributed learning can be achieved through a combination of stochastic gradient descent and mean field variational inference over random graph samples. A fine-grained comparison is done against a state of the art baseline on real world data.