Extrapolative Collaborative Filtering Recommendation System with Word2Vec for Purchased Product for SMEs

Kyoung Jun Lee, Yujeong Hwangbo, Baek Jeong, Jiwoong Yoo and Kyung Yang Park
Additional contact information
Kyoung Jun Lee: Department of Big Data Analytics, Kyung Hee University, Seoul 02447, Korea
Yujeong Hwangbo: Department of Social Network Science, Kyung Hee University, Seoul 02447, Korea
Baek Jeong: Department of Big Data Analytics, Kyung Hee University, Seoul 02447, Korea
Jiwoong Yoo: AI & BM Lab, Seoul 02449, Korea
Kyung Yang Park: Harex InfoTech, Seoul 04625, Korea

Sustainability, 2021, vol. 13, issue 13, 1-11

Abstract: Many small and medium enterprises (SMEs) want to introduce recommendation services to boost sales, but they need to have sufficient amounts of data to introduce these recommendation services. This study proposes an extrapolative collaborative filtering (ECF) system that does not directly share data among SMEs but improves recommendation performance for small and medium-sized companies that lack data through the extrapolation of data, which can provide a magical experience to users. Previously, recommendations were made utilizing only data generated by the merchant itself, so it was impossible to recommend goods to new users. However, our ECF system provides appropriate recommendations to new users as well as existing users based on privacy-preserved payment transaction data. To accomplish this, PP2Vec using Word2Vec was developed by utilizing purchase information only, excluding personal information from payment company data. We then compared the performances of single-merchant models and multi-merchant models. For the merchants with more data than SMEs, the performance of the single-merchant model was higher, while for the SME merchants with fewer data, the multi-merchant model’s performance was higher. The ECF System proposed in this study is more suitable for the real-world business environment because it does not directly share data among companies. Our study shows that AI (artificial intelligence) technology can contribute to the sustainability and viability of economic systems by providing high-performance recommendation capability, especially for small and medium-sized enterprises and start-ups.

Keywords: extrapolative collaborative filtering; multi-merchant; recommendation system; Word2Vec (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/13/13/7156/pdf (application/pdf)
https://www.mdpi.com/2071-1050/13/13/7156/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:13:y:2021:i:13:p:7156-:d:582302

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().