## Reviewer 1 **Relevance to SIGIR:** 4 (good) **Relevance to SIGIR (comments):** Paper is in the full scope of the conference. **Originality of the work:** 4 (good) **Originality of the work (comments):** The idea of the paper is interesting, combining ideas from the vision. **Technical soundness:** 4 (good) ### Technical soundness (comments) The technical soundness of the paper is good, covering most of the relevant aspects. **Quality of presentation:** 4 (good) ### Quality of presentation (comments) The overall presentation is good, the organization of the figures and tables can be improved. **Adequacy of citations:** 4 (good) **Adequacy of citations (comments):** AlI relevant citations are included. **Reproducibility of methods:** 2 (poor) **Reproducibility of methods (comments):** The sorucecode is not available decreasing the reproducibility of the paper. ### Strengths The idea of the paper is interesting, applying concepts from vision for inferring missing interactions. The evaluation section convers reasonable baselines, exploring various aspects of the method on standard datasets. Paper presents both idea and theoretical background behind the method. Overall the paper presents a solid work (including the presentation). ### Weaknesses Despite only 2 metrics are used, the results are reported only for @k=20. Such large k values do not reflect real-world application and it is not clear whether the improvements are gained on 19th-20th position or on 1st-2nd. This would make a great difference in real application, but form the results it is not visible. The organization of the paper can be improved. The figures and tables are sometimes placed on strange place, which do not contribute to the readability of the paper (e.g., Table 2). The source code is notmade available,which greatly reduces the replicability of the paper and method. **Overall recommendation:** 1 (weak accept) ### Detailed comments to authors Authors aim at proposing method for imputing missing interaction in the collaborative filtering setting. The idea of the proposal is interesting, authors suggest using the concept of blurring and in the next phase sharpening of the user item matrix. The paper is generally well written easy to follow (see the shortcoming section), however the source code is not made publicly available, thus greatly reducing the reproducibility of the paper. The method is introduced from both perspectives - the idea and also theoretical background. Authors position the method within the state-of-the-art and in the evaluation section use these as the baselines. The list of baselines covers most of the relevant methods. One of the shortcomings of the evaluation is that authors report metrics only for k=20, which is unrealistic in the real world scenario. Moreover, from the comparison point of view, the method which brings improvement on 20th is better, but this improvement is not so relevant for the real-world application. The evaluation may be skewed by this. To sum it up, the paper presents a solid work, with interesting idea. The method is compared with relevant baselines over standard dataset. I encourage authors to address the shortcomings in the next version of the paper, primarily focusing on the improving the reproducibility of the paper and to report metrics for different k values respectively. ## Reviewer 2 **Relevance to SIGIR:** 5 (excellent) **Relevance to SIGIR (comments):** none **Originality of the work:** 4 (good) **Originality of the work (comments):** none **Technical soundness:** 4 (good) **Technical soundness (comments):** none **Quality of presentation:** 5 (excellent) **Quality of presentation (comments):** none **Adequacy of citations:** 4 (good) **Adequacy of citations (comments):** none **Reproducibility of methods:** 4 (good) **Reproducibility of methods (comments):** none ### Strengths 1) The authors proposed a Blurring- Sharpening Process Model (BSPM), which can blur the user-item interaction matrix, and sharpen the blurred matrix to derive unknown user-item interactions. It is worth mentioning that this method can provide recommendation results in a very short time without training. 2) The paper is explained very well with easy-to-follow logical flow. 3) The authors compare the proposed method with 43 popular CF methods on the three benchmark datasets. Experiment section is comprehensive and helps answer most of the concerns a reader may have. ### Weaknesses 1) Although the performance of BSPM can be better than 43 existing CF methods, and the novelty and coverage are also improved. I think the paper lacks the explanation of the theoretical support, such as how tail items are affected during the ODEs solution procedure, resulting in the improvement of evaluation metrics. Rather than the reverse explanation from the results. 2) The proposed model is not significantly sensitive to parameters T_b and T_b/t of the blurring process, and the model is sensitive to sharpening process parameters. Some more in-depth analysis should be done to illustrate this conclusion, maybe a visualization. 3) While blurring and sharpening of the model have been shown to be effective through experiments, the authors should detailed discuss the intuition behind the decisions to choose them for recommendation - qualitative comments. **Overall recommendation:** 1 (weak accept) ### Detailed comments to authors Overall, it is hard for me to completely understand the novelty of contributions in different parts of the model. Please refer to my comments of weakness. And the incremental original contribution of the authors isn't clear to me in that context. Anyway, from the experimental results, it performs optimally enough for the recommended scenarios. In addition, the recommendation model without training stage can reduce resource consumption and opens a new era of recommendation systems. ## Reviewer 3 **Relevance to SIGIR:** 4 (good) ### Relevance to SIGIR (comments) b. This paper focuses on a blurring-sharpening process models for collaborative filtering, which is directly relevant to the topics of SIGIR. ### Originality of the work Siis wolk takes a fresh perspective on colaborative filtering, which l think is very novel. **Technical soundness:** 4 (good) **Technical soundness (comments):** The model is sound **Quality of presentation:** 4 (good) **Quality of presentation (comments):** The presentation is good and the paper is easy to follow. **Adequacy of citations:** 4 (good) **Adequacy of citations (comments):** Citations are appropriate for the paper. **Reproducibility of methods:** 4 (good) **Reproducibility of methods (comments):** Reproducibility is good. ### Strengths 1.The paper introduces a novel perspective on collaborative filtering by using the Blurring-Sharpening Process model, which is an interesting and innovative approach. 2.The model design in the paper is clear and detailed, providing sufficient information for readers to understand and potentially replicate the study. 3.The experimental results presented in the paper are impressive, as the proposed approach outperforms multiple baseline models on multiple datasets. ### Weaknesses 1.Although the empirical results are promising, the paper lacks sufficient theoretical analysis to explain why the Blurring-Sharpening Process model is effective in improving collaborative filtering. 2.The issue of computational cost is not fully addressed in the paper, particularly in regards to the potential memory usage of real-world user-item interaction matrices. 3.The paper does not appear to address the cold-start problem, which is an important challenge in collaborative filtering. **Overall recommendation:** 1 (weak accept) ### Detailed comments to authors This paper proposed a Blurring-Sharpening Process model to address collaborative filtering problem. The empirical results are promising. However, there is still some problems: 1. Lack sufficient theoretical analysis. We are not sure if such good experimental results are supported by theory. 2. The computational cost problem, the user-item interaction matrix in the real world is very large and may result in very large memory consumption. 3. How do you handle cold start problem? Can the same process be applied to new users? Despite some problems, I really appreciate the approach proposed in this paper and I think it provide a very interesting and valuable perspective. ## Meta-review ### Meta-review The reviewers give positive scores with relatively higher confidence. The reviewers recognize that the authors propose novel concept, conduct technically robust experiments, and that the paper is well-written. Two reviewers point out that this paper needs more theoretical analysis, which would make this work much better. The authors should cite the following work for nDCG: K. Järvelin and J. Kekäläinen: "Cumulated Gain-Based Evaluation of IR Techniques" ACM Transactions on Information Systems (TOIS), 20(4): 422-446, 2002. In addition, while the authors employ a lot of baselines, newer approaches may outperform older ones. So the meta-reviewer would like to suggest to the authors that they need to carefully select the baselines to make the comparative experiments much more reasonable. Anyway, this is a very good work that proposes novel concept. So the meta-reviewer recommends "accept" for this work.