Customer-Category Interest Model: A Graph-Based Collaborative Filtering Model with Applications in Finance

Abstract

The financial domain naturally contains multiple different types of entities such as stocks, product categories, investment participants, intermediaries, and customers, and interactions between these entities. This paper introduces a Graph-based Collaborative Filtering Category Recommendation (GCFCR) system as a first step in modelling the financial domain as an inter-connected, heterogeneous, dynamic system of nodes and edges. The goal of this paper is to identify customer interest based on the neighborhood of each node and make personalized suggestions or identify relevant content for each customer. Matching relevant products and services to customers is a key foundation of building and maintaining strong customer relationships, facilitating more personalized marketing which can ultimately result in increased customer activity, trust, and revenue. In this paper, we run a set of experiments to compare different recommendation techniques, concluding that the proposed GCFCR approach outperforms in this real-life application.