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Deep learning based hashtag recommendation system for multimedia data

Abstract

This work aims to provide a novel hybrid architecture to suggest appropriate hashtags to a collection of orpheline tweets. The methodology starts with defining the collection of batches used in the convolutional neural network. This methodology is based on frequent pattern extraction methods. The hashtags of the tweets are then learned using the convolution neural network that was applied to the collection of batches of tweets. In addition, a pruning approach should ensure that the learning process proceeds properly by reducing the number of common patterns. Besides, the evolutionary algorithm is involved to extract the optimal parameters of the deep learning model used in the learning process. This is achieved by using a genetic algorithm that learns the hyper-parameters of the deep architecture. The effectiveness of our methodology has been demonstrated in a series of detailed experiments on a set of Twitter archives. From the results of the experiments, it is clear that the proposed method is superior to the baseline methods in terms of efficiency.
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Category

Academic article

Language

English

Author(s)

  • Youcef Djenouri
  • Asma Belhadi
  • Gautam Srivastava
  • Jerry Chun-Wei Lin

Affiliation

  • SINTEF Digital / Mathematics and Cybernetics
  • Kristiania University of Applied Sciences
  • Western Norway University of Applied Sciences
  • China Medical University
  • Brandon University

Year

2022

Published in

Information Sciences

ISSN

0020-0255

Volume

609

Page(s)

1506 - 1517

View this publication at Norwegian Research Information Repository