Parameterized Explanations for Investor / Company Matching

TL;DR

This paper introduces an explainable recommendation system for matching investors and companies, leveraging representation learning to perform well on small datasets and providing parameterized explanations to enhance trust and adoption.

Contribution

It presents a novel combination of representation learning and parameterized explanation generation for investor-company matching, addressing data scarcity and explainability.

Findings

System performs well compared to human recommendations

Explainability improves real-life adoption

Effective on small financial datasets

Abstract

Matching companies and investors is usually considered a highly specialized decision making process. Building an AI agent that can automate such recommendation process can significantly help reduce costs, and eliminate human biases and errors. However, limited sample size of financial data-sets and the need for not only good recommendations, but also explaining why a particular recommendation is being made, makes this a challenging problem. In this work we propose a representation learning based recommendation engine that works extremely well with small datasets and demonstrate how it can be coupled with a parameterized explanation generation engine to build an explainable recommendation system for investor-company matching. We compare the performance of our system with human generated recommendations and demonstrate the ability of our algorithm to perform extremely well on this task. We also highlight how explainability helps with real-life adoption of our system.