Personalized Recommendation for Crowdfunding Platform: A Multi-objective Approach

Crowdfunding is an emerging Internet fundraising platform in which creators post descriptions of their projects and investors glance over these projects to support or not. With the increasing amount of projects posted in the crowdfunding platform, it is necessary to develop personalized recommendation systems (RSs) by suggesting suitable projects to crowdfunding investors. In this paper, we propose a personalized recommender system for crowdfunding platform to make accurate, high profitable and diverse project recommendations for crowdfunding investors. Specifically, the task of personalized recommendation for crowdfunding platform is modeled as a multi-objective optimization problem. The proposed model maximizes two conflicting performance metrics named as utility-accuracy and topic-diversity. The utility-accuracy is obtained by the probabilistic spreading method, while the topic-diversity is evaluated by recommendation coverage. Then, a multi-objective evolutionary algorithm for personalized recommendation in crowdfunding platform (termed as MOEA-PRCP) is proposed for the two-objective optimization problem. In MOEA-PRCP, a novel initialization strategy is designed for speeding the convergence of the proposed algorithm. Extensive experiments are conducted on a real-world crowdfunding data collected from Indiegogo.com, and the experimental results clearly demonstrate the effectiveness of MOEA-PRCP for personalized recommendation in crowdfunding platform.