Product Supply Optimization for Crowdfunding Campaigns

Recent years have witnessed the rapid development of Finance Internet platforms, specifically, crowdfunding, which is for creators designing campaigns (projects) to collect funds from public. Usually, the limited budget of a creator is manually divided into several perks (reward options), which should fit various market demand and further bring different monetary contributions for the campaign. Therefore, it is very challenging for each creator to design an effective allocation of perks when launching a campaign. Indeed, our aim is to enhance the funding performance of newly proposed campaigns, with a focus on optimizing the product supply of perks. In this paper, given the expected budget and the perks of a campaign, we propose a novel solution to automatically recommend the optimal product supply for balancing the expected return of this campaign against the risk. Along this line, we define it as a constrained portfolio selection problem, where the investment volume is measured by a multi-task learning method, and we adopt two kinds of methods for task splitting. Furthermore, we extend the investment volume prediction model with inner competition for capturing the competitive relationship between the perks in one campaign. Finally, extensive experiments on real-world crowdfunding data clearly prove the performance significantly.