This preprint is under consideration at EURASIP Journal on Wireless Communications and Networking. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
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Research
User tag weight assessment based on fuzzy theory in mobile social networks
Li Xu
Fujian Normal University
Corresponding Author
ORCiD: https://orcid.org/0000-0002-8972-3373
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Mobile social network supports mobile communication and asynchronous social networking. For enterprises, how to provide better services and create greater business value through the data and information provided by users is crucial. For example, enterprises need to build user profiles to achieve personalized recommendation and precision marketing. In view of the data modeling stage of user profile, we propose a method to evaluate user tag weight, which includes two steps. Specifically, we introduce fuzzy theory to get the initial weight interval. Then, genetic algorithm with single point crossover is used to optimize user tag weight. Experiment results show that the proposed method has better performance than other three methods applied to recommendation system.
Keywords
User Tag Weight, User Profiling, Mobile Social Networks, Fuzzy Theory, Genetic Algorithm
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Peer Review Timeline
CURRENT STATUS: Under Review
Version 2
Posted 10 Jan, 2021
No community comments so far
Reviewers invited
Invitations sent on 05 Jan, 2021
Editor assigned
On 26 Dec, 2020
Submission checks complete
On 26 Dec, 2020
Editor invited
On 26 Dec, 2020
No comments provided
Editorial decision: Major revision
On 24 Nov, 2020
Reviewer #2 agreed
On 23 Nov, 2020
Review #2 received
Received 23 Nov, 2020
Review #1 received
Received 14 Nov, 2020
Reviewers invited
Invitations sent on 09 Oct, 2020
Reviewer #1 agreed
On 09 Oct, 2020
Editor assigned
On 19 Aug, 2020
Editor invited
On 18 Aug, 2020
Submission checks complete
On 15 Aug, 2020
First submitted
On 12 Aug, 2020
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Subject Areas
Systems and Networking
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This preprint is under consideration at EURASIP Journal on Wireless Communications and Networking. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research
User tag weight assessment based on fuzzy theory in mobile social networks
Li Xu
Fujian Normal University
Corresponding Author
ORCiD: https://orcid.org/0000-0002-8972-3373
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 2
Posted 10 Jan, 2021
No community comments so far
Reviewers invited
Invitations sent on 05 Jan, 2021
Editor assigned
On 26 Dec, 2020
Submission checks complete
On 26 Dec, 2020
Editor invited
On 26 Dec, 2020
No comments provided
Editorial decision: Major revision
On 24 Nov, 2020
Reviewer #2 agreed
On 23 Nov, 2020
Review #2 received
Received 23 Nov, 2020
Review #1 received
Received 14 Nov, 2020
Reviewers invited
Invitations sent on 09 Oct, 2020
Reviewer #1 agreed
On 09 Oct, 2020
Editor assigned
On 19 Aug, 2020
Editor invited
On 18 Aug, 2020
Submission checks complete
On 15 Aug, 2020
First submitted
On 12 Aug, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Systems and Networking
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Mobile social network supports mobile communication and asynchronous social networking. For enterprises, how to provide better services and create greater business value through the data and information provided by users is crucial. For example, enterprises need to build user profiles to achieve personalized recommendation and precision marketing. In view of the data modeling stage of user profile, we propose a method to evaluate user tag weight, which includes two steps. Specifically, we introduce fuzzy theory to get the initial weight interval. Then, genetic algorithm with single point crossover is used to optimize user tag weight. Experiment results show that the proposed method has better performance than other three methods applied to recommendation system.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the latest manuscript can be downloaded and accessed as a PDF.