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.
Book Recommendation System using Matrix Factorization
