Paper:
DropoutNet: Addressing Cold Start in Recommender Systems
Latent models have become the default choice for recommender systems due to their performance and scalability. However, research in this area has primarily focused on modeling user-item interactions, and few latent models have been developed for cold start. Deep learning has recently achieved remarkable success showing excellent results for diverse input types. Inspired by these results we propose a neural network based latent model called DropoutNet to address the cold start problem in recommender systems. Unlike existing approaches that incorporate additional content-based objective terms, we instead focus on the optimization and show that neural network models can be explicitly trained for cold start through dropout. Our model can be applied on top of any existing latent model effectively providing cold start capabilities, and full power of deep architectures. Empirically we demonstrate state-of-the-art accuracy on publicly available benchmarks. Code is available at https://github.com/layer6ai-labs/DropoutNet.

Review:
Excellent paper, presenting an good idea with sufficient experiments demonstrating its efficiency. Well written and with a good survey of related work. 
The paper addresses the following problem: when mixing id level information (user id of item id) with coarser user and item features, any model has the tendency to explain most of the training data with the fine grained id features, using coarse features only to learn the rarest ids. As a result, the generated model is not good at inference when only coarser features are available (cold-start cases, of a new user or a new item). The paper proposes a dial to control how much the model balances out the fine grained and coarser information via drop-out of fine grained info while training.
The loss function in Equation (2) has the merit of having a label value for all user-item pairs, by taking the output of the low rank model U_u V_v ^T as labels. My concern with such a loss is that the predictions from the low rank model have very different levels of accuracy: they are very accurate for the most popular users and the most popular items (users and items with most data), but can be quite noisy in the tail, and the loss function in Equation (2) does not account for the confidence in the low rank model prediction. Replacing the sum by a weighted sum using as weights some simple estimates of the confidence in each low rank model prediction could improve the overall model. But this can be done in future work.
Question about experiments on CiteULike: Figure 2 and Table 1 well support the point the author is making about the effect of dropout on recall for both, warm start cases and cold start cases. However, it is surprising that the cold start recall numbers are better than the warm start recall numbers. Can the authors explain this phenomenon?
Minor details:
Line 138: f_\cal{U} -> f_\cal{V}
Line 349: is able is able -> is able