Venue: NeurIPS 2022 Conference

Reviews:

final_score: rating: 5: Borderline accept: Technically solid paper where reasons to accept outweigh reasons to reject, e.g., limited evaluation. Please use sparingly.
review_content: summary: This work proposed an approach to model the dynamic user-item interaction graphs. Ths proposed approach is a framework comprising an online-offine architechture with majorly matrix-factorization techniques.
strengths_and_weaknesses: I like the combination of matrix factorization and dynamic graph-structured data. The experiment results demonstrate that this combination does have a positive impact.

The idea of parameter-free is always exciting in data analysis. However, I cannot fully agree with the definition proposed by the author(s). I expect to see a model without or with less complicated hyper-parameter or training parameter set-ups. However, according to the definition:

"...we do not incorporate any parameters which need to be learned via back-propagation..." the reality is far from the expectation.

In the end, this work still requrieds a search of (a wide range of) hyper-parameters. Given the required computation for model training, it can be better if the author(s) can provide more insight on how to choose the hyper-parameters (e.g. rule-of-thumb) instead of through experiments.
questions: I understand it is tedious to prepare the code and data sets. But I still strongly suggest the authors provide the code and data sets to make the process more transparent. Maybe there are some concerns (e.g. ethical, anonymity) the author(s) can elaborate more in the author rebuttal phase?
limitations: I have my reservation related to the content of checklist where the author(s) wrote "To the best of our knowledge, this work does not have potential negative social impacts." I like the confidence signaled. However, when it comes to user-item data analysis, a common ethical issue is that some users and items may suffer because they belong to some under represented groups. Especially when the training data is splited into fragments. I think the paper can be better if the author(s) elaborate more on this direction and futhermore the potentials of this work or continuations of this work can help mediate the mentioned issue.
review_title: null

final_score: rating: 6: Weak Accept: Technically solid, moderate-to-high impact paper, with no major concerns with respect to evaluation, resources, reproducibility, ethical considerations.
review_content: summary: This paper introduces a parameter-free dynamic graph embedding (FreeGEM) method for link prediction tasks. For modeling user preference in dynamic integration graphs, there are two crucial factors to consider: 1) collaborative relationships among users and 2) user personalized interaction patterns. Existing methods usually implicitly consider these two factors together and suffer from noisy distributions. The proposed method considers the two factors separately instead: with an incremental graph embedding engine to model the former and a personalized dynamic interaction pattern modeler to model the latter. For the incremental graph embedding engine, an Online-Monitor-Offline architecture is proposed, with an online module to approximate the user/item embeddings, a monitor module to estimate the errors of the online module, and an offline module to calibrate the embeddings, such that the module can learn the embeddings efficiently without exceeding the error threshold. Extensive experiments are conducted to demonstrate the effectiveness and efficiency of the proposed method. 
strengths_and_weaknesses: Strengths:

- Originality: The proposed method with the two modules to model the two factors and the Online-Monitor-Offline architecture is novel. 

- Quality: The content of the paper is well organized. The motivation is clear. Sufficient experiments and ablation studies are conducted. 

- Significance: This paper presents a new method that can estimate the user/item embedding for link prediction tasks efficiently. 

Weaknesses:

The methodology part needs more clarification. Some equations or concepts are not well-explained. I point out these issues in Questions.

questions: 1) In  section 4.2, it says u^(r) is the user embedding as r-th time step. Since it never appears in the paper before, is u^(r) a learned embedding from the model? In section 3.2, it also uses the notation u as a one-hot vector indicating the user id. Are they the same thing?
2) In section 3.1.1, it is not clear for me how E_I and E_U are involved in obtaining the interaction matrix(R-hat) with the low-frequency approximation. Can you explain more about the logic flow here?
3) Through the description of section 3,  it seems that Online-Monitor-Offline architecture eventually estimates the interaction matrix. How do you transform the interaction matrix to user/item embedding? Some clarifications are recommended here. 
4) It would be helpful to label all the equations in this paper. 
5) Line 155, a missing space between the sentences.

limitations: The authors adequately addressed the limitations and potential negative societal impact of their work.
review_title: null

final_score: rating: 7: Accept: Technically solid paper, with high impact on at least one sub-area, or moderate-to-high impact on more than one areas, with good-to-excellent evaluation, resources, reproducibility, and no unaddressed ethical considerations.
review_content: summary: The authors present a parameter-free graph embedding model to handle the missing link prediction tasks, including the future-item recommendation and the next-item recommendation. In short, the framework consists of 1) a graph embedding module and 2) a personalization module, where the former is built with an online-monitor-offline structure, and the latter introduces the concept of time decay and attention.
strengths_and_weaknesses: Strength
- The idea is novel and interesting.
- The proposed model achieves a good improvement in efficiency.
- The experiments are comprehensive, including the evaluation of two link prediction tasks, the ablation studies, and results regarding the efficiency. 

Weakness
- Some parts of the paper are unclear. 
1. In line 181, why π/3 is the boundary?
2. In line 183, what does 1-3 or 1’-3’ mean?
3. In line 215, there are variant ways to do time decay. Why this one? 
4. In table 5, is it possible to apply H and I without D/E/F/G?
- It is a bit hard to totally understand the key to the solution as the whole model contains many submodules, which are not comparable to other existing models. For example, the decay mechanism is essential, but do some other models not consider it? I suggest the authors compile a table explaining the differences among the compared models.
questions: Is it possible to apply such an architecture to parameterized models?

limitations: Yes.
review_title: null

Meta-review: metareview: Reviewers were overall on the side of acceptance although not strongly so, with one reviewer being only borderline.

Several positive aspects of the paper were appreciated:
- The idea was considered novel and the motivation clear.
- The combination of matrix factorization with dynamic graph-structured data,
 was appreciated, and the methodological use of the two modules and online-monitor-offline architecture were considered novel.
- The efficiency improvement was appreciated.
- The experiments were considered comprehensive and sufficient.
- The paper was considered well organized.

On the negative side,
- Only addressing link prediction was considered too limited.
- The method was criticized as still requiring search across hyperparameters.
- More clarification of the methodology was desired, and the role of the different submodules compared to functionalities in other methods was considered hard to understand.
- Lack of provided code and data sets was criticized; a partial code release was provided by authors in their response. uRAL
- Potential negative social impacts for underrepresented groups could have been mentioned.

Overall, I think this paper can be acceptable for NeurIPS if the authors take the reviewer comments and the follow-up discussion into account in their final manuscript.
confidence: Less certain
Decision: Accept