Real-time Recommendation System for Stock Investment Decisions

Recommendation systems have become omnipresent, helping people making decisions in various areas. While most of the systems can give accurate recommendations, their learning procedures can be time-consuming. In some cases, this is not permissible; for example when the information about the items and users changes very fast in time. In this paper, we discuss a new recommendation engine, based on labelled property graph knowledge representation and attributed network embeddings, which calculates real-time recommendations for stock investment decisions. In particular, we demonstrate an application of the DANE (dynamic attributed network embedding) framework proposed by Li et al. and show the promising results of the system.