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Dear Authors,
Thank you for the revision. Your paper was accepted following the second peer review process.
Best wishes,
[# PeerJ Staff Note - this decision was reviewed and approved by Daniel S. Katz, a PeerJ Computer Science Section Editor covering this Section #]
The structure/formatting are appropriate, and the paper is well-organized. The abstract, introduction, methodology, results, and references sections are all well-written and meet the required standards for publication.
The experimental design is also well-designed.
The findings of the study are valid and reliable. The results are presented clearly. Overall, the study's findings contribute to the existing knowledge in the field.
I have reviewed the revised version of the paper, and I am pleased to recommend it for publication. The authors have taken into account the comments and suggestions provided and have made appropriate revisions. Based on their efforts, I believe this paper is now suitable for publication.
Authors has done requested revision
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Dear author,
Your article has a few issues. We encourage you to address the concerns and criticisms of the reviewers and resubmit your article once you have updated it accordingly.
Best wishes,
[# PeerJ Staff Note: The review process has identified that the English language must be improved. PeerJ can provide language editing services - please contact us at [email protected] for pricing (be sure to provide your manuscript number and title) #]
[# PeerJ Staff Note: Please ensure that all review comments are addressed in a rebuttal letter and any edits or clarifications mentioned in the letter are also inserted into the revised manuscript where appropriate. It is a common mistake to address reviewer questions in the rebuttal letter but not in the revised manuscript. If a reviewer raised a question then your readers will probably have the same question so you should ensure that the manuscript can stand alone without the rebuttal letter. Directions on how to prepare a rebuttal letter can be found at: https://peerj.com/benefits/academic-rebuttal-letters/ #]
There are several grammatical problems within the text.
Furthermore, all figures require high resolutions printings. Also, check the text and fonts in Figures.
Methodology is not well designed. For readers to quickly catch your contribution, it would be better to highlight major difficulties and challenges, and your original achievements to overcome them, in a clearer way in the abstract and introduction. Also, the methodology section is also not well presented.
I feel that authors have some how contributed through their proposed study in the proposed area of trave recommendations.
the manuscripts should be recommended for publication after minor revision with grammatical mistakes, and methodology design.
In order to effectively utilize the highest potential value of tourism data available on the Internet, that is, multi-modal data providing larger capacity and richer content on social media, this paper proposes a new travel recommendation algorithm based on multi-modal data mining, which provides greater economic and social value for the development of tourism. Overall the paper seems to be written well and having some novel contributions , however, it can be improved further by the following suggestions:
(1) In the abstract part of this paper, it is suggested to use experimental data to present the results of the algorithm.
(2) The references related to the work in section 2 of the paper are superscripts, while the references in sections 3 and 4 are not superscripts, such as [2] and [2]. Please check and unify the format.
(3) Formulas (1),(2) and (3) on page 5 of the paper all use Dirichlet function, but the definition and function of the function are not explained in the paper, so it is suggested to supplement.
(4) Why does this paper adopt the multivector semantic segmentation technique? Tell me the advantages of this technique.
(5) In Section 4.2, 1261 tourists are selected to evaluate the system, but the data amount is too small.
(6) There are too few relevant experimental contents in this paper, so it is suggested to add relevant model evaluation indexes, such as R, F1, etc.
(7) Formula (8) in Section 3 of this paper fuses text features and image features for output. Is it classification or regression? What is the output result? Please elaborate.
(8) In the conclusion part of the paper, it is suggested to increase the concrete achievements, advantages and future development prospects of the tourism recommendation model.
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