A Nonparametric Latent Factor Model For Location-Aware Video Recommendations

TL;DR

This paper introduces a Bayesian nonparametric latent factor model for location-aware video recommendations, effectively capturing the relationship between viewing patterns and geographical location in large-scale datasets.

Contribution

It proposes a scalable Bayesian nonparametric approach for modeling customer preferences based on viewing history and location, with demonstrated effectiveness on real-world data.

Findings

Model captures location-based viewing preferences

Scales efficiently to large datasets

Reveals meaningful relationships between location and preferences

Abstract

We are interested in learning customers' video preferences from their historic viewing patterns and geographical location. We consider a Bayesian latent factor modeling approach for this task. In order to tune the complexity of the model to best represent the data, we make use of Bayesian nonparameteric techniques. We describe an inference technique that can scale to large real-world data sets. Finally we show results obtained by applying the model to a large internal Netflix data set, that illustrates that the model was able to capture interesting relationships between viewing patterns and geographical location.