Business transaction recommendation for discovering potential business partners using deep learning

Abstract

Potential business partner (BP) recommendation is one of the most important issues for companies to increase sales opportunities by discovering new candidate buyers. Recommendation at a low cost and automation is especially essential for small and medium businesses. However, identifying potential BPs has been regarded as a challenging task since not only an analysis of business characteristics for each candidate company by human experts is required but also an investigation of all the possible combinations of their matchings is necessary. Therefore, in this paper, we propose novel BP recommendation models, called deep business partner recommendation (DBR) models, that aim to automatically suggest potential BPs. Specifically, deep learning technique is applied to understand hidden transaction patterns between companies with various industrial sectors and product properties through the two-phases involving i) BP relation representation phase and ii) training and testing phase. In the former, for each company, its features including the industrial sector, product property, relative transaction volume, and geographical distance are embedded into a vector for utilizing as the input of the proposed models. In the latter, the proposed DBR models repeatedly use the input values to capture the hidden transaction patterns between companies in the training. In the testing phase, the extensive experiments conducted to evaluate the BP recommendation performances of the proposed DBR models using a real-world dataset consisting of transaction records among companies in South Korea. The experiment results show that the suggested DBR models significantly outperform the conventional models, in terms of the accuracy for the BP recommendation tasks.