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Multimodal Movie Recommendation System Using Deep Learning

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  • Yongheng Mu

    (Hubei Key Laboratory of Intelligent Geo-Information Processing, College of Computer Science, China University of Geosciences, Wuhan 430078, China
    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China)

  • Yun Wu

    (Hubei Key Laboratory of Intelligent Geo-Information Processing, College of Computer Science, China University of Geosciences, Wuhan 430078, China)

Abstract

Recommendation systems, the best way to deal with information overload, are widely utilized to provide users with personalized content and services with high efficiency. Many recommendation algorithms have been researched and deployed extensively in various e-commerce applications, including the movie streaming services over the last decade. However, sparse data cold-start problems are often encountered in many movie recommendation systems. In this paper, we reported a personalized multimodal movie recommendation system based on multimodal data analysis and deep learning. The real-world MovieLens datasets were selected to test the effectiveness of our new recommendation algorithm. With the input information, the hidden features of the movies and the users were mined using deep learning to build a deep-learning network algorithm model for training to further predict movie scores. With a learning rate of 0.001, the root mean squared error (RMSE) scores achieved 0.9908 and 0.9096 for test sets of MovieLens 100 K and 1 M datasets, respectively. The scoring prediction results show improved accuracy after incorporating the potential features and connections in multimodal data with deep-learning technology. Compared with the traditional collaborative filtering algorithms, such as user-based collaborative filtering (User-CF), item-based content-based filtering (Item-CF), and singular-value decomposition (SVD) approaches, the multimodal movie recommendation system using deep learning could provide better personalized recommendation results. Meanwhile, the sparse data problem was alleviated to a certain degree. We suggest that the recommendation system can be improved through the combination of the deep-learning technology and the multimodal data analysis.

Suggested Citation

  • Yongheng Mu & Yun Wu, 2023. "Multimodal Movie Recommendation System Using Deep Learning," Mathematics, MDPI, vol. 11(4), pages 1-12, February.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:4:p:895-:d:1063900
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    References listed on IDEAS

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    1. Jacoby, Jacob & Speller, Donald E & Berning, Carol A Kohn, 1974. "Brand Choice Behavior as a Function of Information Load: Replication and Extension," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 1(1), pages 33-42, June.
    2. Jingdong Liu & Won-Ho Choi & Jun Liu, 2021. "Personalized Movie Recommendation Method Based on Deep Learning," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-12, February.
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