PONE-D-21-07884

Multitask Feature Learning Approach for Knowledge Graph Enhanced Recommendations with RippleNet

PLOS ONE

Dear Dr. Zhang,

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We look forward to receiving your revised manuscript.

Kind regards,

Qi Zhao

Academic Editor

PLOS ONE

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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

Reviewer #3: Yes

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

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: 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

Reviewer #3: 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

Reviewer #3: 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 combines the relevant technologies of depth recommendation and knowledge graph, the knowledge graph as side Information, and combining it with the recommender system in two forms. In this paper, use two models are combined to make up for their shortcomings and maximize the advantages of the models.

(1) In Sec.2, the Related Work are introduced. While displaying the relevant technologies, it is also necessary to clarify why these technologies are used. For example, explain the role of Side information and so on.

(2) As for the illustration of the proposed model, fig2 is not a good one. Firstly, it’s hard to recognize the input of the model and the relation between different modules. Secondly, the meanings of symbols and different color parts are not given.

(3) The same symbol expresses too much meaning, for example, the meaning of L and K in Eq.4 is not elaborated.

(4) The figure presented in this paper needs to be explained as comprehensively as possible. Include the meaning of each module and the links between the modules, should be given as comprehensive as possible

(5) As we know, DKN is designed specially for news recommendation where the entities are extracted from news titles. Obviously less entities could be extracted from movie names since they are often shorter than news titles. These need to be explained in the results. The experiment shows that the accuracy of recommendation is greatly improved。

Reviewer #2: In this paper, the authors have described the Ripp-MKR deep learning recommendation model in detail, the key algorithms of the Ripp-MKR model which combine joint learning and alternate learning methods in KG-aware recommendations. The result indicates that the Ripp -MKR model has significant advantages over other baseline models. The paper is well organized. The introduction gave a satisfactory literature survey on the similar topic and it outlined the proposed method well. On my point of view, the idea of this work is original and interesting to deep network recommendation systems.

(1) There are many notations in this paper, which should be clearly defined:

What do L and K represent respectively in Eq.4?

What do o_u^1 represent respectively in Eq.11?

(2) Some misused words need to be corrected such as:

Recommendation Modul ->Recommendation Module

KGE Modul ->KGE Module

“The relationship structure between the knowledge graph and the recommender system is shown in Tab.1” Should change Tab.1 to Fig.1.

(3) Since Figure 2 is the main structure of the model proposed in this paper, it needs to be introduced emphatically. Figure 2 should clarify the inputs and outputs of the model, the meaning of the symbols and the parts with different colors need to be explained.

(4) The format of the Figure mentioned in the body should be uniform. Cite figures with the format: Fig 1A, Fig 1B, Fig 2, Fig 3, etc. The cite to Figure 2 and Figure 3 in this article is incorrectly formatted. The name of Figure 1 is too long, so there is no need to explain the figure in the title.

(5) In Sec.6, the parameter of baseline should give reasonable meanings. Such as explain the meaning of λ in each baseline model.

Reviewer #3: This paper studies the recommendation system, which aims to incorporate knowledge graph embedding learning into recommendation systems. In order to combine joint learning and alternate learning of knowledge graph into recommendation systems, the authors proposed a multitask feature learning approach for knowledge graph enhanced recommendations with RippleNet. In addition, cross and compress units are used to share latent features and learn the high-order interactions among items in recommendation systems and entities in the knowledge graph. Finally, the model combines the knowledge graph with the historical interaction record of a user’s historical records to represent the users’ characteristics.

Shortcomings of this paper:

(1) There is no need to introduce the details of recommender system (RS) categories, side information, etc. in Sec.1, which should be removed into Sec.2. For the Sec.1, only a few technologies need to be mentioned, mainly explaining the significance and purpose of the model design.

(2)The authors should elaborate more about their solution's motivations and rationales. For example, why did they combine joint learning and alternating learning? What is the advantage of such process compared with previous KG-based RS? What is the objective of corss&compress units?

(3) Notations and explanations should not only be shown in the table, but also explained in terms of the notation after the formula is listed. Although it has been mentioned above, the explanation of important formula symbols still needs to be mentioned again

(4) The writing of this paper is somewhat lacking due to some grammar and format errors and typos So I suggest the authors proof read their manuscript.

(5) Figure 2~5 cannot be found in the paper, only figure title here.

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Reviewer #1: Yes: Zhen Liu

Reviewer #2: No

Reviewer #3: No

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Multitask Feature Learning Approach for Knowledge Graph Enhanced Recommendations with RippleNet

PONE-D-21-07884R1

Dear Dr. 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 #2: All comments have been addressed

Reviewer #3: All comments have been addressed

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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 #2: Yes

Reviewer #3: Yes

**********

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

Reviewer #2: Yes

Reviewer #3: Yes

**********

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 #2: Yes

Reviewer #3: Yes

**********

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 #2: Yes

Reviewer #3: Yes

**********

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 #2: (No Response)

Reviewer #3: I believe the authors have adequately addressed the comments raised in the previous round of review. At this stage, I have no further points to make.

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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 #2: No

Reviewer #3: No