Deep learning has profoundly impacted many areas of machine learning. However, it took a while for its impact to be felt in the field of recommender systems. In this article, we outline some of the challenges encountered and lessons learned in using deep learning for recommender systems at Netflix. We first provide an overview of the various recommendation tasks on the Netflix service. We found that different model architectures excel at different tasks. Even though many deep-learning models can be understood as extensions of existing (simple) recommendation algorithms, we initially did not observe significant improvements in performance over well-tuned non-deep-learning approaches. Only when we added numerous features of heterogeneous types to the input data, deep-learning models did start to shine in our setting. We also observed that deep-learning methods can exacerbate the problem of offline–online metric (mis-)alignment. After addressing these challenges, deep learning has ultimately resulted in large improvements to our recommendations as measured by both offline and online metrics. On the practical side, integrating deep-learning toolboxes in our system has made it faster and easier to implement and experiment with both deep-learning and non-deep-learning approaches for various recommendation tasks. We conclude this article by summarizing our take-aways that may generalize to other applications beyond Netflix.

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We are interested in Bayesian models for collaborative filtering that incorporate side-information or metadata about items in addition to user-item interaction data. We present a simple and flexible framework to build models for this task that exploit the low-rank structure in user-item interaction datasets. Although the resulting models are non-conjugate, we develop an efficient technique for approximating posteriors over model parameters using variational inference. We borrow the "re-parameterization trick" from Bayesian deep learning literature to enable variational inference in our models. The resulting approximate Bayesian inference algorithm is scalable and can handle large scale datasets. We demonstrate our ideas on three real world datasets where we show competitive performance against widely used baselines.

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