Collaborative Deep Embedding via Dual Networks
Despite the long history of research on recommender systems, current approaches still face a number of challenges in practice, e.g. the difficulties in handling new items, the high diversity of user interests, and the noisiness and sparsity of observations. Many of such difficulties stem from the lack of expressive power to capture the complex relations between items and users. This paper presents a new method to tackle this problem, called Collaborative Deep Embedding. In this method, a pair of dual networks, one for encoding items and the other for users, are jointly trained in a collaborative fashion. Particularly, both networks produce embeddings at multiple aligned levels, which, when combined together, can accurately predict the matching between items and users. Compared to existing methods, the proposed one not only provides greater expressive power to capture complex matching relations, but also generalizes better to unseen items or users. On multiple real-world datasets, this method outperforms the state of the art.
The responses to the pre-review questions are not strong; especially w.r.t. the question about dataset density and why the dataset had to be subsampled, the authors responded that subsampling is common in recommender systems work, including the papers cited. This isn*t a particularly strong justification of why subsampling is a good idea, and in particular doesn*t answer the question of *how would the results look without subsampling,* which I think is a question that could easily have been answered directly. Especially given that the goal of dealing with the cold-start issue is so heavily emphasized in the paper, in seems odd to sample the data to reduce sparsity. Other than that, the pre-review questions seem to have been answered satisfactorily. The contribution of the paper is to propose user and item embedding methods, as a means of learning complex non-linear interactions between users and items. This is fairly similar to recent work on deep RS, though the network formulation has some differences. Overall this is an reasonably put together paper that makes a contribution in an important area, though there are still some shortcomings that should be addressed, namely: 1) The evaluation is unusual. Recall@M is the only result reported, though this is not usually an evaluation seen in recommender systems research. At the very least other performance measures (rmse or AUC) should be reported for completeness, even if the results are not strong 2) Given that the contribution is fairly simple (i.e., the *standard* recommender systems task, but with a new model) it*s a shame that unusual data samples have to be taken. This should be a case where it*s possible to report results against competing methods using *exactly* the same data they used, and exactly the same error measure, for the fairest comparison possible. Without the above it*s hard to tell how much the performance improvements are really due to the method being better, versus the choice of datasets and the choice of loss functions.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Collaborative Deep Embedding via Dual Networks
Despite the long history of research on recommender systems, current approaches still face a number of challenges in practice, e.g. the difficulties in handling new items, the high diversity of user interests, and the noisiness and sparsity of observations. Many of such difficulties stem from the lack of expressive power to capture the complex relations between items and users. This paper presents a new method to tackle this problem, called Collaborative Deep Embedding. In this method, a pair of dual networks, one for encoding items and the other for users, are jointly trained in a collaborative fashion. Particularly, both networks produce embeddings at multiple aligned levels, which, when combined together, can accurately predict the matching between items and users. Compared to existing methods, the proposed one not only provides greater expressive power to capture complex matching relations, but also generalizes better to unseen items or users. On multiple real-world datasets, this method outperforms the state of the art.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



The responses to the pre-review questions are not strong; especially w.r.t. the question about dataset density and why the dataset had to be subsampled, the authors responded that subsampling is common in recommender systems work, including the papers cited. This isn*t a particularly strong justification of why subsampling is a good idea, and in particular doesn*t answer the question of *how would the results look without subsampling,* which I think is a question that could easily have been answered directly. Especially given that the goal of dealing with the cold-start issue is so heavily emphasized in the paper, in seems odd to sample the data to reduce sparsity. Other than that, the pre-review questions seem to have been answered satisfactorily. The contribution of the paper is to propose user and item embedding methods, as a means of learning complex non-linear interactions between users and items. This is fairly similar to recent work on deep RS, though the network formulation has some differences. Overall this is an reasonably put together paper that makes a contribution in an important area, though there are still some shortcomings that should be addressed, namely: 1) The evaluation is unusual. Recall@M is the only result reported, though this is not usually an evaluation seen in recommender systems research. At the very least other performance measures (rmse or AUC) should be reported for completeness, even if the results are not strong 2) Given that the contribution is fairly simple (i.e., the *standard* recommender systems task, but with a new model) it*s a shame that unusual data samples have to be taken. This should be a case where it*s possible to report results against competing methods using *exactly* the same data they used, and exactly the same error measure, for the fairest comparison possible. Without the above it*s hard to tell how much the performance improvements are really due to the method being better, versus the choice of datasets and the choice of loss functions.
assistant
The responses to the pre-review questions are not strong; especially w.r.t. the question about dataset density and why the dataset had to be subsampled, the authors responded that subsampling is common in recommender systems work, including the papers cited. This isn*t a particularly strong justification of why subsampling is a good idea, and in particular doesn*t answer the question of *how would the results look without subsampling,* which I think is a question that could easily have been answered directly. Especially given that the goal of dealing with the cold-start issue is so heavily emphasized in the paper, in seems odd to sample the data to reduce sparsity. Other than that, the pre-review questions seem to have been answered satisfactorily. The contribution of the paper is to propose user and item embedding methods, as a means of learning complex non-linear interactions between users and items. This is fairly similar to recent work on deep RS, though the network formulation has some differences. Overall this is an reasonably put together paper that makes a contribution in an important area, though there are still some shortcomings that should be addressed, namely: 1) The evaluation is unusual. Recall@M is the only result reported, though this is not usually an evaluation seen in recommender systems research. At the very least other performance measures (rmse or AUC) should be reported for completeness, even if the results are not strong 2) Given that the contribution is fairly simple (i.e., the *standard* recommender systems task, but with a new model) it*s a shame that unusual data samples have to be taken. This should be a case where it*s possible to report results against competing methods using *exactly* the same data they used, and exactly the same error measure, for the fairest comparison possible. Without the above it*s hard to tell how much the performance improvements are really due to the method being better, versus the choice of datasets and the choice of loss functions.