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DOI: 10.14569/IJACSA.2023.0140206
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Sequence Recommendation based on Deep Learning

Author 1: Gulsim Rysbayeva
Author 2: Jingwei Zhang

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 14 Issue 2, 2023.

  • Abstract and Keywords
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Abstract: Sequence recommendation systems have become increasingly popular in various fields such as movies and social media. These systems aim to predict a user's preferences and interests based on their past behavior and provide them with personalized recommendations. Deep learning, particularly Recurrent Neural Networks (RNNs), have emerged as a powerful tool for sequence recommendation. In this research, we explore the effectiveness of RNNs in movie and Instagram recommendation systems. We investigate and compare the performance of different types of RNNs, such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), in recommending movies and Instagram posts to users based on their browsing history. Additionally, we study the impact of incorporating additional information such as user's demographics and Instagram hashtags on the performance of the recommendation system. We also evaluate the performance of RNN-based movie and Instagram recommendation systems in comparison to traditional approaches, such as collaborative filtering and content-based filtering, in terms of accuracy and personalization. The findings of this research provide insights into the effectiveness of RNNs in movie and Instagram recommendation systems and contribute to the development of more accurate and personalized recommendations for users.

Keywords: Long short-term memory (LSTM) and gated recurrent unit (GRU); RNN; deep learning; recommendation systems

Gulsim Rysbayeva and Jingwei Zhang, “Sequence Recommendation based on Deep Learning” International Journal of Advanced Computer Science and Applications(IJACSA), 14(2), 2023. http://dx.doi.org/10.14569/IJACSA.2023.0140206

@article{Rysbayeva2023,
title = {Sequence Recommendation based on Deep Learning},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140206},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140206},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {2},
author = {Gulsim Rysbayeva and Jingwei Zhang}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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