International business matching using word embedding

Abstract

Recommender systems, which help users discover information or knowledge they might need without requiring them to have specific previous knowledge, are gaining popularity in our age of information overload. In addition, natural language processing techniques like word embedding offer new possibilities for extracting information from a massive amount of text data. This work explores the possibility of applying word embedding as the foundation for a recommender system to help international businesses identify appropriate counterparts for their activities. In this paper, we describe our system and report preliminary experiments using Wikipedia as a corpus. Our experiments attempt to provide answers to support business decision makers when they are considering entering a relatively unknown market and are seeking better understanding or appropriate partners. Our experiment shows promising results that will pave the way for future research.