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Recommending K-Wave Items Tailored for Small-Sized Exporters by Incorporating Dense and Sparse Vectors

Author

Listed:
  • Jimin Lee

    (Department of AI and Big Data, Soonchunhyang University, Asan 31538, Republic of Korea)

  • Eunjeong Na

    (School of Computer Engineering, Hansung University, Seoul 02876, Republic of Korea)

  • Keejun Han

    (School of Computer Engineering, Hansung University, Seoul 02876, Republic of Korea)

  • Donggil Na

    (Intelligent Convergence Research Laboratory, Electronics and Telecommunications Research Institute, Daejeon 34129, Republic of Korea)

Abstract

As K-wave has been strengthened via recent K-contents, K-wave items such as cosmetics and electronic devices have also gained attention globally. For small-sized export sellers who purchased the items and exported them to different countries, it is significant to discover which K-wave items are trending in specific countries. To do so, we proposed an ensemble recommender system by producing the dense vector, which is generated by a variant of Bidirectional Encoder Representations from Transformers (BERT), and balancing the vector with a sparse vector in order to ensure the efficient execution speed and recommendation accuracy. Based on the data we have collected specifically for potential K-items, our experiment showed that the proposed model outperforms the various baselines, which are used for content-based filtering.

Suggested Citation

  • Jimin Lee & Eunjeong Na & Keejun Han & Donggil Na, 2023. "Recommending K-Wave Items Tailored for Small-Sized Exporters by Incorporating Dense and Sparse Vectors," Sustainability, MDPI, vol. 15(22), pages 1-16, November.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:22:p:16098-:d:1283404
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