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

Jimin Lee, Eunjeong Na, Keejun Han () and Donggil Na ()
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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

Sustainability, 2023, vol. 15, issue 22, 1-16

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.

Keywords: K-wave; BERT; export sellers; trending; recommender systems; content-based filtering; dense vector; sparse vector (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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