Recommendation Algorithm of Crowdfunding Platform Based on Collaborative Filtering
Bixi Wang, Wenbin Wu, Wenfeng Zheng, Yan Liu, Lirong Yin
Abstract
Abstract With the continuous progress of Internet technology, the crowdfunding platform has become a new way of network financing. While the generated data keeps increasing, its benefit does not increase in a proportional way, resulting in the “information overload” phenomenon. The personalized recommendation system can solve this problem by mining users’ interests and preferences from a large amount of data. It has achieved success in many fields. This paper applies machine learning algorithm to build a recommendation system based on collaborative filtering. The designed personalized recommendation algorithm can provide accurate and rapid personalized recommendation services, which is convenient for users and conducive to the development of the crowdfunding platform. In addition, this paper uses the data from the crowdfunding platform in practice to complete the performance verification of the algorithm.