Understanding the Impact of Startups’ Features on Investor Recommendation Task via Weighted Heterogeneous Information Network

Abstract

Investor recommendation is a critical and challenging task for startups, which can assist startups in locating suitable investors and enhancing the possibility of obtaining investment. While some efforts have been made for investor recommendation, few of them explore the impact of startups’ features, including partners, rounds, and fields, to investor recommendation performance. Along this line, in this paper, with the help of the heterogeneous information network, we propose a FEatures’ COntribution Measurement approach of startups on investor recommendation, named FECOM. Specifically, we construct the venture capital heterogeneous information network at first. Then, we define six venture capital metapaths to represent the features of startups that we focus on. In this way, we can measure the contribution of startups’ features on the investor recommendation task by validating the recommendation performance based on different metapaths. Finally, we extract four practical rules to assist in further investment tasks by using our proposed FECOM approach.