Group Recommendation Based on Financial Social Network for Robo-Advisor

IEEE Access

Jianping Yin¹  En Zhu²  Qiang Liu²  Jingming Xue² 

[1] College of Computer, Dongguan University of Technology, Dongguan, China;College of Computer, National University of Defense Technology, Changsha, China;

关键词: Asset allocation;    social network;    collaborative filtering;    group recommender systems;   

DOI  :  10.1109/ACCESS.2018.2871131

来源: DOAJ

【 摘 要 】

Robo-advisor is a financial advisor that can get help from machine-learning algorithms to automatically analyze financial product risk levels and provide portfolio recommendations. In the previous work, robo-advisor mainly focused on the basic information and investment preferences of individual users and often ignored the relationship between groups and the individual's risk preference. In the actual environment, the individual investment behavior and the group's social relations are inseparable. In order to solve this challenge, this paper proposes a group recommendation model based on financial social networks and collaborative filtering algorithms. Compared with the latest personalized recommendation system, it not only considers the asset status and risk level of individual investors but also considers social relationships and risk levels among groups. With experiments on benchmark and real-world datasets, we demonstrate that the proposed algorithm achieves the superior performance on both the tasks compared to the state-of-the-art methods.

【 授权许可】

Unknown