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A Proposal of Deep Learning Model for Classifying User Interests on Social Networks

Authors:

Xuan Truong Dinh,

Hai Van PhamAuthors Info & Claims

ICMLSC '20: Proceedings of the 4th International Conference on Machine Learning and Soft Computing

Pages 10 - 14

https://doi.org/10.1145/3380688.3380707

Published: 07 March 2020 Publication History

Abstract

In the recent years, there are huge data extracted from social networks in both static and real-time analysis, such as Facebook, Twitter, LinkedIn, and Instagram. Recently, most researchers have investigated in classifying textual contents without user interests/behaviors from huge data of social networks. This paper has presented a novel approach using a Convolution Neural Network with its new contribution of user perceptions from the social network data to classifying the user interests. Experimental results show that the proposed model performs better than the conventional algorithms in terms of classifying user interests. Additionally, the proposed model enhances a quality of classification for user interests tracking real-time in social networks.

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Cited By

Lazzez OQahtani AAlsufyani AAlmutiry ODhahri HPiuri VAlimi A(2023)DeepVisInterests : deep data analysis for topics of interest predictionMultimedia Tools and Applications10.1007/s11042-023-14806-282:26(40913-40936)Online publication date: 4-Apr-2023
https://doi.org/10.1007/s11042-023-14806-2
Bulathwela SPérez-Ortiz MYilmaz EShawe-Taylor J(2022)Power to the Learner: Towards Human-Intuitive and Integrative Recommendations with Open Educational ResourcesSustainability10.3390/su14181168214:18(11682)Online publication date: 17-Sep-2022
https://doi.org/10.3390/su141811682
Van Duong PDinh XSon LVan Hai P(2022)Enhancement of Gravity Centrality Measure Based on Local Clustering Method by Identifying Influential Nodes in Social NetworksMultimedia Technology and Enhanced Learning10.1007/978-3-031-18123-8_48(614-627)Online publication date: 19-Oct-2022
https://doi.org/10.1007/978-3-031-18123-8_48
Show More Cited By

Index Terms

A Proposal of Deep Learning Model for Classifying User Interests on Social Networks
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Machine learning theory

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Recent research has focused on extracting personal interest data from social media. Although many methods have been developed, accurately estimating users' interests is often difficult because messages on social media are short and are not classified ...

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ICMLSC '20: Proceedings of the 4th International Conference on Machine Learning and Soft Computing

January 2020

175 pages

ISBN:9781450376310

DOI:10.1145/3380688

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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NICT: National Institute of Information and Communications Technology

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Published: 07 March 2020

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ICMLSC 2020

ICMLSC 2020: The 4th International Conference on Machine Learning and Soft Computing

January 17 - 19, 2020

Haiphong City, Viet Nam

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Cited By

Lazzez OQahtani AAlsufyani AAlmutiry ODhahri HPiuri VAlimi A(2023)DeepVisInterests : deep data analysis for topics of interest predictionMultimedia Tools and Applications10.1007/s11042-023-14806-282:26(40913-40936)Online publication date: 4-Apr-2023
https://doi.org/10.1007/s11042-023-14806-2
Bulathwela SPérez-Ortiz MYilmaz EShawe-Taylor J(2022)Power to the Learner: Towards Human-Intuitive and Integrative Recommendations with Open Educational ResourcesSustainability10.3390/su14181168214:18(11682)Online publication date: 17-Sep-2022
https://doi.org/10.3390/su141811682
Van Duong PDinh XSon LVan Hai P(2022)Enhancement of Gravity Centrality Measure Based on Local Clustering Method by Identifying Influential Nodes in Social NetworksMultimedia Technology and Enhanced Learning10.1007/978-3-031-18123-8_48(614-627)Online publication date: 19-Oct-2022
https://doi.org/10.1007/978-3-031-18123-8_48
Abbas A(2021)Social network analysis using deep learning: applications and schemesSocial Network Analysis and Mining10.1007/s13278-021-00799-z11:1Online publication date: 25-Oct-2021
https://doi.org/10.1007/s13278-021-00799-z
Dinh XTrinh TNgoc TPham V(2021)Improving Social Trend Detection Based on User Interaction and Combined with Keyphrase Extraction Using Text Features on Word GraphIntelligent Systems and Networks10.1007/978-981-16-2094-2_21(163-170)Online publication date: 13-May-2021
https://doi.org/10.1007/978-981-16-2094-2_21
Van Pham HTien D(2021)Hybrid Louvain-Clustering Model Using Knowledge Graph for Improvement of Clustering User’s Behavior on Social NetworksIntelligent Systems and Networks10.1007/978-981-16-2094-2_16(126-133)Online publication date: 13-May-2021
https://doi.org/10.1007/978-981-16-2094-2_16

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