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All the reviewers comments were considered in the revised manuscript.
I am writing to inform you that your manuscript has been Accepted for publication. Congratulations!
The work is clear, author contributions to stat-of-art is reasonable.
Experiments cover theory.
results are reasonable
- The level of English language of the paper is good enough
- The paper is rich with the contents and has up to date literature references
- Paper is well structured.
- The significant result have met the objective
The experimental and methodology that presented in the paper are well defined
The finding of the paper and conclusions are well stated, which linked to objectives of research paper and supported the arguments
The contribution of the paper is a publishable values
No comment
No comment
No comment
The authors have done well to follow all comments and made all the necessary corrections.
Revise the manuscript based on the reviewers comments.
Paper is well introduced and written in clear English.
Experiments cover theoretical hypothesis.
Findings are valid as authors claims with given significance evidence.
- The English language that used in this article is clear and unambiguous.
- The literature that are provided in the article are sufficient and up to date.
- clear methodology for tackling and solving the problem with good structure.
- The datasets and raw data that were used are real-world datasets, which are clear defined and cited.
- The presented result are met the hypotheses of the paper
no comment
no comment
- The term collaborative that is mentioned in the title is not being defined and used in the article. in deed is not illustrated to improve the performance of filtering
- The cluster formation is not explain well, how many clusters? based on what? etc.
no comment
no comment
no comment
The paper is well written, and it has the potential to change the way temporal drifting and sparsed datasets are treated. The authors have developed a method to apply a Latent-based Temporal Optimization (LTO) approach to improve the prediction accuracy of CF by learning the past attitudes of users and their interests over time. The methodology part is well explained. However, few issues are raised.
- the results need to be improved. The reasons behind each finding should be explained by the support of previous related studies or authors' justifications.
- there exist some typo errors in figures 9 and 10. Specifically, in fig 9, the label font type is not consistent with the ones used in other figures. Also, no scaling is provided at the horizontal axis. The authors may need to scale the axes appropriately. While in fig 10, the horizontal axis title is not very clear.
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