Paper:
Hashing-based collaborative filtering learns binary vector representations (hash codes) of users and items, such that recommendations can be computed very efficiently using the Hamming distance, which is simply the sum of differing bits between two hash codes. A problem with hashing-based collaborative filtering using the Hamming distance, is that each bit is equally weighted in the distance computation, but in practice some bits might encode more important properties than other bits, where the importance depends on the user. To this end, we propose an end-to-end trainable variational hashing-based collaborative filtering approach that uses the novel concept of self-masking: the user hash code acts as a mask on the items (using the Boolean AND operation), such that it learns to encode which bits are important to the user, rather than the user's preference towards the underlying item property that the bits represent. This allows a binary user-level importance weighting of each item without the need to store additional weights for each user. We experimentally evaluate our approach against state-of-the-art baselines on 4 datasets, and obtain significant gains of up to 12% in NDCG. We also make available an efficient implementation of self-masking, which experimentally yields <4% runtime overhead compared to the standard Hamming distance.

Review:
The author proposes a variational encoder (VAE) based approach to perform hashing-based collaborative filtering, which focuses on the weighting problem of each hash-code bit by applying the "self-masking" technique. This proposed technique modifies the encoded information in hash codes and avoids the additional storage requirements and floating point computation, while preserving the efficiency of bit manipulations thanks to hardware-based acceleration. An end-to-end training is achieved by resorting the well-known discrete gradient estimator, straight-through (ST) estimator. 
Strength:
The idea of employing the discrete VAE framework to perform hashing-based collaborative filtering is interesting. From the perspective of applications, I think it is somewhat novel.
The experimental results demonstrate the performance superiorities of the self-masking hashing-based collaborative filtering method. 
Weakness:
I have concerns over using the VAE to model the ratings of each user and item pairs here. Essentially, you are building a VAE for every rating value R_u, i.  Although the parameters are shared, the VAE’s have their own prior. For generative models like VAE, they need to learn from a lot of data, not just one data point. From the perspective of generating data looking similar to the training data, I don’t think your model have learned anything. Only the reconstruction part is important to your model. 
The idea of using discrete VAE for hashing tasks has been explored before, see “NASH: Toward End-to-End Neural Architecture for Generative Semantic Hashing”.  It employed a very similar idea, although it is not used for CF, but for hashing directly. The novelties of the model is limited.
Since ratings are essentially ordinal data, using Gaussian distribution to model the rating data may be not appropriate.
The whole paper, especially the model, is not presented well. The model is not presented in a rigorous way, and some sentences in this paper are difficult to follow.
The runtime analysis is not sufficient. In addition to comparing with the methods using hamming distance, we also want to see the advantages of the hamming based method over the real-value based method on speed acceleration.
Other question:
To realize the self-masking role, the paper proposed to use the function f(z_u, z_i)=g(Hamming_self-mask(z_u, z_i)), Equ. 12, and demonstrate the effectiveness of using this function by experiments. Since the necessity of using self-masking is not very convincing, I doubt whether this self-masking function f(z_u, z_i) is indispensable. Maybe, if some other functions that takes z_u and z_i as input are used, better results may be observed.