Paper:
Cold-start and efficiency issues of the Top-k recommendation are critical to largescale recommender systems. Previous hybrid recommendation methods are effective to deal with the cold-start issues by extracting real latent factors of cold-start items(users) from side information, but they still suffer low efficiency in online recommendation caused by the expensive similarity search in real latent space. This paper presents a collaborative generated hashing (CGH) to improve the efficiency by denoting users and items as binary codes, which applies to various settings: cold-start users, cold-start items and warm-start ones. Specifically, CGH is designed to learn hash functions of users and items through the Minimum Description Length (MDL) principle; thus, it can deal with various recommendation settings. In addition, CGH initiates a new marketing strategy through mining potential users by a generative step. To reconstruct effective users, the MDL principle is used to learn compact and informative binary codes from the content data. Extensive experiments on two public datasets show the advantages for recommendations in various settings over competing baselines and analyze the feasibility of the application in marketing.

Review:
The work considers the problem of efficient user and item recommendations in the warm- and cold-start settings. It aims at improving computational efficiency of the best candidate selection in these settings by utilizing binary codes representation. The transformation from an actual to a binary code representation is learned in a hybrid manner using both collaborative and content information. In order to keep such representations compact yet expressive enough, the authors impose a set of constraints that ensure balanced and uncorrelated transformations. Once binary codes are learned, the inference can be made by virtue of efficient Hamming distance computations. Moreover, the search for candidate entities can be performed via the generative step that projects binary codes onto actual feature space, where kNN-based techniques can be further utilized.
Major drawback of the work is that it does not provide any quantitative evidence to support the main claim – that the proposed approach is at least more computationally efficient, since it underperforms competing methods in terms of accuracy. Essentially, the work answers the question whether it is possible to utilize hashing techniques based on binary codes; however, the question on the practicality and efficiency of this approach remains open. I would therefore suggest rejecting the work.
One of the weak points of other methods noted by the authors is “the expensive similarity search in real latent space”. The authors aim to resolve that problem by learning hashing functions based on “compact and informative” binary codes representation. However, while an overall problem formulation is clearly described and the learning objective is well explained, no further evidence supporting initial claims is provided. Moreover, an overall logic seems contradictory:
1.	Binary codes allow efficient preference estimation via XOR operation.
2.	Learning binary codes is a difficult discrete optimization task.
3.	Hence, we employ special MDL principle for solving a constraint optimization problem and employ relaxation of hash codes to move away from discrete optimization.
After relaxation, the hash codes are no longer binary. Do you still enforce binary representation by some thresholding or other method? If yes, more words explaining this should be added to the text. If no, then how is it different from classical learning of latent variables? Essentially, relaxed non-binary hash codes are similar to latent vectors.
The lack of description raises concerns in an overall efficiency and the authors, unfortunately, provide no evidence of improved computational performance. Given that the proposed approach underperforms competing methods in terms of accuracy of recommendations, more efforts should be made to demonstrate its competitive advantages in terms of time required for training and generating predictions.
Another contradictory part is the generative step for candidate selection. The idea of using inference to generate the most pertinent user vector for a selected item hash code is novel and interesting. However, it requires searching neighbors in the real feature space, which can be very inefficient, depending on the structure of features. I’m not convinced that it is better than searching neighbors directly in the latent space, which can be done in the majority of hybrid models. Moreover, there exist various approximate nearest-neighbors search methods, e.g. Annoy, NMSLib, Faiss, etc., which allow trading-off accuracy and efficiency. Considering that hash codes also lose some information (which is observed in the results of experiments), it seems necessary to have a comparison with these approximate methods as well.
It should be also noted that in some cases you don’t even have to run the similarity search. Many hybrid models learn latent representation of features directly and cold-start entities are straightforwardly described via combination of the corresponding latent vectors of their features (e.g, Factorization Machines [Rendle 2009]). Hence, affinity between a cold item and some user can be quickly estimated via inner product of their latent vectors. 
Suggestions on improving the work:
Worth mentioning, that recent studies raise certain concerns about the superiority of modern neural network-based approaches over simpler (and properly tuned) linear baselines, see the work by [Dacrema, Cremonesi, Jannach 2019] on “A Worrying Analysis of Recent Neural Recommendation Approaches”. The DropoutNet method in your experiments is very similar to CDL in terms of accuracy. The latter, however, underperforms even simple knn models, as shown by the work mentioned above. The haven’t tested it in the cold-start regime, though. Still, I’d strongly recommend adding to your experiments comparison with simpler hybrid models, e.g., Factorization Machines. Also note that there are even stronger baselines published recently, e.g. HybridSVD by [Frolov, Oseledets 2019].
Additional remarks:
1)	Figure 3 and the related text seem to focus on too obvious things. Indeed, by increasing the number of entities to compare against, you increase chances to have a hit. This part of the text, basically, states that the method works, which can already be seen from other results.
2)	A lot of attention is given to the “marketing application”. It’s ok to have it in introduction and make a connection to the real-world problem; however, further mentions of it in the text feel excessive. In the experiment section you describe a standard evaluation procedure for the cold start, there is no need to refer to marketing application again as you do not provide any new metric. It would feel much more organic if you would have the results of A/B testing on real users. Otherwise, I’d suggest to focus more on the problem that you’re solving, not on possible application.
3)	The text is in a very unpolished state. It reads more like a draft version. There are many typos and error both in text and in derivations.
References:
Rendle, Steffen. "Factorization machines." In 2010 IEEE International Conference on Data Mining, pp. 995-1000. IEEE, 2010.
Frolov, Evgeny, and Ivan Oseledets. "HybridSVD: when collaborative information is not enough." In Proceedings of the 13th ACM Conference on Recommender Systems, pp. 331-339. ACM, 2019.
Dacrema, Maurizio Ferrari, Paolo Cremonesi, and Dietmar Jannach. "Are we really making much progress? A worrying analysis of recent neural recommendation approaches." In Proceedings of the 13th ACM Conference on Recommender Systems, pp. 101-109. ACM, 2019.