PONE-D-21-25428MNI: An enhanced multi-task neighborhood interaction model for recommendation on knowledge graphPLOS ONE

Dear Dr. Hao Zhang,

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Qi Zhao

Academic Editor

PLOS ONE

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Upon internal assessment of your manuscript, we found that it is similar to one of your previous works published in PLOS ONE: "Multitask Feature Learning Approach for Knowledge Graph Enhanced Recommendations with RippleNet".

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

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3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

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4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: This paper describes a recommendation system on knowledge graph, which deploys neighbor-neighbor interactions to explore high-order relations. Then the algorithm applies a cross & compress unit to combine the recommendation system and knowledge graph, whereby those two modules can interact and share latent features to improve the recommendation precision.

Pros:

The algorithm explains well understood and the experiment supports the algorithm assumption and shows improvement compared with other baselines.

Cons:

1.Figure 2 is the whole structure of the framework, however, needs more explanations in the paragraph.

2.There are some mistakes of English grammar in the abstract and the introduction part, needs to correct them. For example, Therefore, researchers tend to integrate side information which includes user… “which” should be changed to “that”

3.The future work should be discussed in detail.

4.The caption of Figure 1 is not self-explained. The caption should state which one is user, item, or attribute.

5.In the experiment, Figure 5-7 the yellow line is not clear enough to recognize. Also, the captions should be in more detail.

Reviewer #2: The algorithm MNI adopts the separation of recommendation system and knowledge graph, and use a bridge unit to share each latent feature. It also applies neighbor-neighbor reconstruction instead of user-item interaction, containing more information about user-user interaction, item-item interaction, and a deep understanding of the high-order relations.

However, some minor correction should be made:

1. Reference format is not consistent. The authors should double check and revise it.

2. The algorithm 1 on page 10 should be explained more in detail. It is fuzzy when just looking at the algorithm.

3. The blocks and colors in the figure 2 is not well explained. Are there any different meaning about different shapes and colors? The author needs to be clearer.

4. About the recommendation module, the author use 2-layer GAT. Could you explain GAT as for the reader is hard to follow.

5. Minor typos, e.g., Missing space between citation and text.

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Reviewer #1: Yes: Zhen Liu

Reviewer #2: No

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MNI: An enhanced multi-task neighborhood interaction model for recommendation on knowledge graph

PONE-D-21-25428R1

Dear Dr. Hao ZHANG,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Qi Zhao

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: (No Response)

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: (No Response)

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: (No Response)

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4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: (No Response)

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: (No Response)

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The authors have expounded the opinions I put forward in the last round of review, and the data description in the manuscript can also technically support the author's conclusions. It is hoped that the authors will check the English expression before the official publication of the manuscript.

Reviewer #2: (No Response)

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7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No