Venue: ICLR 2023 Conference

Reviews:

final_score: rating: 5: marginally below the acceptance threshold
review_content: summary_of_the_paper: The authors study the problem of federated recommendation with implicit feedback, and propose a new framework with personalized score function and personalized item embedding (without user embedding), which is illustrated in Figure 1(c).
strength_and_weaknesses: Strength:
1 The idea of using personalized item embedding in federated recommendation is new (though it is not new in traditional recommendation).

Weakness:
1 For key issues in federated recommendation, the authors do not contribute/discuss much, e.g., communication cost, privacy protection, time complexity.

2 The technical contribution is limited. For example, the contents of Section 4 are not about a formal and principled solution, but most about heuristics.

3 For the studied problem, there are many recent works, which are not studied in the experiments.
clarity,_quality,_novelty_and_reproducibility: The paper is well presented, which is easy to be understood.

It is not difficult to reproduce the results (though not very easy). The authors are courageed to include more details about empirical studies, e.g., parameter configurations and data preprocessing.

summary_of_the_review: The authors study an important problem of federated recommendation with implicit feedback, and propose a new framework with personalized score function and personalized item embedding.

My major concern is that the technical contribution compared with existing works on federated recommendation is limited.

Minors:
optimization framework optimization framework
It is a federated version of FedNCF
review_title: null

final_score: rating: 6: marginally above the acceptance threshold
review_content: summary_of_the_paper: This paper presents a new personalized federated training model architecture that does not contain user embeddings explicitly. The model relies on personalized item embedding and scoring function to achieve the personalized recommendation. When conducting federated training (global), it learns a shared embedding parameter for the items. And, during the personalization step, it conducts 1 step gradient fine-tuning for each client. The term "Dual" personalization simply comes from separately learned  scoring function that never been federated trained. Experiment results show promising performance improvements.
strength_and_weaknesses: The idea seems straightforward and somewhat novel in terms of reducing modeling cost.
The description of this paper is a bit lack polishing. 
I am also concerning the risk of overfitting of this model. 



clarity,_quality,_novelty_and_reproducibility: The paper is fairly written but need some polishing to make it more readable.
Novelty is a bit weak as encoding user representation into network parameters could be interpreted as matrix factorization where user embeddings are scoring function parameter (even the model also personalized a bit item embedding with one-step gradient). 
The work seems reproduceable since algorithm is clearly given. But, I have no much confidence to reproduce the experiment results.

summary_of_the_review: The idea seems straightforward and somewhat novel in terms of reducing modeling cost. But the cost reduction is not significant (as least not demonstrated to be significant).
The description of this paper is a bit lack polishing. And, I have to guess what the authors try to deliver
- 1. does \theta^m contains item embedding? why do you use E and \theta^m together? are they somewhat different?
- 2. why is one-step gradient for each client sufficient? any justification or assumption?
The scoring functions are never transferred and there is no default scoring function. In this case, how do you handle users who lack interactions? I think this need further discussion. 
I am also concerning the risk of overfitting of this model. As the model personalizing (fine-tuning) ALL of the client parameters. The strong collaborative regularization is missing. Considering the datasets used in this paper are often showing very coherent preference (lack diversity) of each user, it is possible that the client model is simply learned to remember the user's historical interactions.
review_title: null

final_score: rating: 3: reject, not good enough
review_content: summary_of_the_paper: To learn a personalized and lightweight recommendation model for each client, this paper proposes to learn user-specific lightweight models that are deployed on the device side. Furthermore, it introduces a dual personalization mechanism to learn personalized item embedding tables and the score function for each user. Experimental results on four real-world datasets demonstrate the effectiveness of the proposed method.
strength_and_weaknesses: Pros:
a)	The motivation of this paper, i.e., learning a personalized recommendation model for each user, is important.
b)	Empirical results demonstrate the effectiveness of the proposed method.
c)	The paper is well-written and easy to follow.

Cons:
a)  This paper argues that the existing federated recommendation models are cumbersome, so it proposes to learn a LIGHTWEIGHT local score function to make personalized predictions on the client side. However, under the recommendation scenario, the item embedding table generally dominates the vast majority of parameters of the model instead of the parameters of the score function. Thus, the proposed method is meaningless.
b)  The training of the personalized score function and the fine-tuning of the item embedding table are performed locally on the device. However, the training data on each device is very limited, which makes it hard to learn an accurate model.
c)  The technical novelty of this paper is very limited. Learning the personalized item embedding table and score functions has been studied in [1]. The authors should clarify the key difference between their work and [1].
d)  Important baselines are missing, such as [1,2]. The authors should compare the proposed method with them.
e)  There is a duplicate phrase, “optimization framework” in the third contribution on page 2.

Reference:
[1] Lin, Yujie, et al. "Meta matrix factorization for federated rating predictions." Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 2020.
[2] Wu, Chuhan, et al. "A federated graph neural network framework for privacy. preserving personalization." Nature Communications 13.1 (2022): 1-10.

clarity,_quality,_novelty_and_reproducibility: a)	Clarity: this paper is well-written and easy to follow.
b)	Quality: the quality of this paper is quite fair due to missing some important baselines.
c)	Novelty: the technical novelty of this paper is very limited
d)	Reproducibility: the authors have provided enough descriptions to reproduce the method.

summary_of_the_review: Due to the limited novelty and missing important related work,  I recommend reject.
review_title: null

final_score: rating: 6: marginally above the acceptance threshold
review_content: summary_of_the_paper: This paper introduces a dual personalization strategy for federated recommendation, which can better handle non-iid user data and improve recommendation performance. The authors propose to use personalized scoring modules and personalized item embeddings on devices, where the model first updates the scoring module and then updates the item embeddings. Although the proposed method is simple, it is surprisingly effective. The authors also provide some discussions on its insight.
strength_and_weaknesses: Strengths:
- This paper is clearly written and easy to follow.
- The proposed method is very simple and easy to use.
- The performance improvement is quite impressive.

Weaknesses:
- It seems that the devices need to maintain a large set of item embeddings, which may be somewhat unscalable. Some discussions on how to reduce the number of locally maintained item embeddings are recommended.
- The authors seem to use sampled metrics over the top recommendation results. In fact, it may not be consistent with the results on the full ranking list (see [1]). 
- It is not clear whether the proposed method still works well for inactive users. Since the scoring module may contain much more parameters than the user embedding, it would require a larger number of samples to train.

[1] Krichene, W., & Rendle, S. (2022). On sampled metrics for item recommendation. Communications of the ACM, 65(7), 75-83.
clarity,_quality,_novelty_and_reproducibility: This work is well conducted with good clarity. The contribution of this work is moderate but focused.
summary_of_the_review: This paper presents a good empirical study on federated recommendation. The proposed method is quite simple, general, and effective. Thus, my recommendation is weak accept, though its technical novelty is not very big.
review_title: null

Meta-review: metareview:_summary,_strengths_and_weaknesses: In this paper, the authors propose a Personalized Federated Recommendation (PFedRec) framework for providing privacy-preserving recommendation services in federated settings. The framework learns many user-specific lightweight models that can be deployed on smart devices, rather than a single heavyweight model on a server. A dual personalization mechanism is also proposed to effectively learn fine-grained personalization on both users and items. The overall learning process is formulated as a unified federated optimization framework. Experiments on multiple benchmark datasets demonstrate the effectiveness of PFedRec and the dual personalization mechanism. Visualizations and analysis of the personalization techniques in item embedding are also provided. The reviewers thought that learning a personalized recommendation model for each user is interesting and important, the paper is easy to follow and  that the paper has nice empirical results demonstrate the effectiveness of the proposed method. The reviewers raised concerns about various claims including that "the item embedding table generally dominates the vast majority of parameters of the model instead of the parameters of the score function", the use of on device learning, lack of substantial technical novelty, missing baseline comparisons. Finally, one reviewer raised the concern that "The authors seem to use sampled metrics over the top recommendation results. In fact, it may not be consistent with the results on the full ranking list (see [1]).". The authors provided a comprehensive response. However, IMO the authors response does not properly address the issues that were raised. In its current form the paper requires substantial revision and therefore I can not recommend acceptance. That said I think is an interesting paper with potential and recommend resubmission to a future venue following the suggestion above.
justification_for_why_not_higher_score: The paper has a few flaws that does not merit a higher score
Decision: Reject