Investment behavior prediction in heterogeneous information network

The crowdfunding industry is growing rapidly worldwide and poses new challenges on how to understand investment behavior. Indeed, a key challenge in this area is how to measure the similarity of an investor and a company, or the interest of an investor in a company. Tremendous effort has been made in previous research regarding the single effective factor or homogeneous network model based on link prediction for investment behavior prediction. In this study, we build an investment behavior prediction model of meta-path-based heterogeneous network, which considers multiple entity and relation types associated with the investment behavior of a particular investor. Our investment behavior prediction model provides an effective similarity measure function for meta-path. To validate the proposed model, we perform experiments on real-world data from CrunchBase. Experimental results reveal that our investment behavior prediction model is indeed a useful indicator.