Attribute-aware Collaborative Filtering: Survey and Classification
Attribute-aware CF models aims at rating prediction given not only the historical rating from users to items, but also the information associated with users (e.g. age), items (e.g. price), or even ratings (e.g. rating time). This paper surveys works in the past decade developing attribute-aware CF systems, and discovered that mathematically they can be classified into four different categories. We provide the readers not only the high level mathematical interpretation of the existing works in this area but also the mathematical insight for each category of models. Finally we provide our preliminary experiment results comparing the effectiveness of the major works in each category.
 This paper surveys models for collaborative filtering with user/item covariate. Overall the authors seems to have captured the essence of a large number of popular CF models and I found that the proposed model classification is reasonable. The notation also make it easy to understand the differences between different models. In that sense this paper could be useful to researchers wanting to better understand this field. It may also be useful to develop further insights into current models (although the authors do not go that route). The impact of this paper may be limited in this community since it is a survey about a fairly niche topic (a subset of recommender systems) that may not be of central interest at ICLR. Overall, I think this paper would be a better fit in a recsys, applied ML or information retrieval journal. A few comments: I find that there are several ways the paper could make a stronger contribution: 1) Use the unifying notation to discuss strengths and weaknesses of current approaches (ideally with insights about possible future approaches). 2) Report the results of a large study of many of the surveyed models on a large number of datasets. Ideally further insights could be derived from these results. 3) Provide a common code framework with all methods 4) Add a discussion on more structured sources of covariates (e.g., social networks). This could probably more or less easily be added as a subsection using the current classification. - A similar classification of collaborative filtering models with covariates is proposed in this thesis (p.41): https://tspace.library.utoronto.ca/bitstream/1807/68831/1/Charlin_Laurent_201406_PhD_thesis.pdf - The paper is well written overall but the current version of the paper contains several typos.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Attribute-aware Collaborative Filtering: Survey and Classification
Attribute-aware CF models aims at rating prediction given not only the historical rating from users to items, but also the information associated with users (e.g. age), items (e.g. price), or even ratings (e.g. rating time). This paper surveys works in the past decade developing attribute-aware CF systems, and discovered that mathematically they can be classified into four different categories. We provide the readers not only the high level mathematical interpretation of the existing works in this area but also the mathematical insight for each category of models. Finally we provide our preliminary experiment results comparing the effectiveness of the major works in each category.
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>



 This paper surveys models for collaborative filtering with user/item covariate. Overall the authors seems to have captured the essence of a large number of popular CF models and I found that the proposed model classification is reasonable. The notation also make it easy to understand the differences between different models. In that sense this paper could be useful to researchers wanting to better understand this field. It may also be useful to develop further insights into current models (although the authors do not go that route). The impact of this paper may be limited in this community since it is a survey about a fairly niche topic (a subset of recommender systems) that may not be of central interest at ICLR. Overall, I think this paper would be a better fit in a recsys, applied ML or information retrieval journal. A few comments: I find that there are several ways the paper could make a stronger contribution: 1) Use the unifying notation to discuss strengths and weaknesses of current approaches (ideally with insights about possible future approaches). 2) Report the results of a large study of many of the surveyed models on a large number of datasets. Ideally further insights could be derived from these results. 3) Provide a common code framework with all methods 4) Add a discussion on more structured sources of covariates (e.g., social networks). This could probably more or less easily be added as a subsection using the current classification. - A similar classification of collaborative filtering models with covariates is proposed in this thesis (p.41): https://tspace.library.utoronto.ca/bitstream/1807/68831/1/Charlin_Laurent_201406_PhD_thesis.pdf - The paper is well written overall but the current version of the paper contains several typos.
assistant
 This paper surveys models for collaborative filtering with user/item covariate. Overall the authors seems to have captured the essence of a large number of popular CF models and I found that the proposed model classification is reasonable. The notation also make it easy to understand the differences between different models. In that sense this paper could be useful to researchers wanting to better understand this field. It may also be useful to develop further insights into current models (although the authors do not go that route). The impact of this paper may be limited in this community since it is a survey about a fairly niche topic (a subset of recommender systems) that may not be of central interest at ICLR. Overall, I think this paper would be a better fit in a recsys, applied ML or information retrieval journal. A few comments: I find that there are several ways the paper could make a stronger contribution: 1) Use the unifying notation to discuss strengths and weaknesses of current approaches (ideally with insights about possible future approaches). 2) Report the results of a large study of many of the surveyed models on a large number of datasets. Ideally further insights could be derived from these results. 3) Provide a common code framework with all methods 4) Add a discussion on more structured sources of covariates (e.g., social networks). This could probably more or less easily be added as a subsection using the current classification. - A similar classification of collaborative filtering models with covariates is proposed in this thesis (p.41): https://tspace.library.utoronto.ca/bitstream/1807/68831/1/Charlin_Laurent_201406_PhD_thesis.pdf - The paper is well written overall but the current version of the paper contains several typos.