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Utilizing Half Convolutional Autoencoder to Generate User and Item Vectors for Initialization in Matrix Factorization

Author

Listed:
  • Tan Nghia Duong

    (School of Electronics and Telecommunications, Hanoi University of Science and Technology, Hanoi 100000, Vietnam
    These authors contributed equally to this work.)

  • Nguyen Nam Doan

    (School of Electronics and Telecommunications, Hanoi University of Science and Technology, Hanoi 100000, Vietnam
    These authors contributed equally to this work.)

  • Truong Giang Do

    (School of Electronics and Telecommunications, Hanoi University of Science and Technology, Hanoi 100000, Vietnam
    These authors contributed equally to this work.)

  • Manh Hoang Tran

    (School of Electronics and Telecommunications, Hanoi University of Science and Technology, Hanoi 100000, Vietnam)

  • Duc Minh Nguyen

    (School of Electronics and Telecommunications, Hanoi University of Science and Technology, Hanoi 100000, Vietnam)

  • Quang Hieu Dang

    (School of Electronics and Telecommunications, Hanoi University of Science and Technology, Hanoi 100000, Vietnam)

Abstract

Recommendation systems based on convolutional neural network (CNN) have attracted great attention due to their effectiveness in processing unstructured data such as images or audio. However, a huge amount of raw data produced by data crawling and digital transformation is structured, which makes it difficult to utilize the advantages of CNN. This paper introduces a novel autoencoder, named Half Convolutional Autoencoder, which adopts convolutional layers to discover the high-order correlation between structured features in the form of Tag Genome, the side information associated with each movie in the MovieLens 20 M dataset, in order to generate a robust feature vector. Subsequently, these new movie representations, along with the introduction of users’ characteristics generated via Tag Genome and their past transactions, are applied into well-known matrix factorization models to resolve the initialization problem and enhance the predicting results. This method not only outperforms traditional matrix factorization techniques by at least 5.35% in terms of accuracy but also stabilizes the training process and guarantees faster convergence.

Suggested Citation

  • Tan Nghia Duong & Nguyen Nam Doan & Truong Giang Do & Manh Hoang Tran & Duc Minh Nguyen & Quang Hieu Dang, 2022. "Utilizing Half Convolutional Autoencoder to Generate User and Item Vectors for Initialization in Matrix Factorization," Future Internet, MDPI, vol. 14(1), pages 1-19, January.
  • Handle: RePEc:gam:jftint:v:14:y:2022:i:1:p:20-:d:717703
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