TY - JOUR
T1 - Modeling behavior sequence for personalized fund recommendation with graphical deep collaborative filtering
AU - Chou, Yi Ching
AU - Chen, Chiao Ting
AU - Huang, Szu-Hao
N1 - Publisher Copyright:
© 2021 Elsevier Ltd
PY - 2022/4/15
Y1 - 2022/4/15
N2 - Financial services are increasingly personalized due to the rapid development of artificial intelligence. In particular, precision marketing is the ultimate goal in providing financial services. However, precision marketing is hindered by problems in model scalability, cold starts, and insufficient transaction data points for many customers and products. Some researchers have attempted to solve these problems by incorporating deep learning into the collaborative filtering algorithm. This has improved the performance of the traditional matrix factorization algorithm, but it comes at the cost of imposing a limit on the capacity of the recommendation model to capture the complex interaction features. In this paper, we propose a graphical deep collaborative filtering (GraphDCF) algorithm for providing personalized mutual fund recommendations. The graph-structured network is constructed by connecting customer nodes with similar purchases and redeemed trading orders in the sequential view. In this manner, we can model different latent relationships among customers who have similar shopping habits. Subsequently, the corresponding embedding vector for each customer node is generated through an aggregate function based on similarity in transaction behaviors. Finally, to provide personalized recommendations, in addition to customer features and mutual fund attributes, the proposed deep embedded collaborative filtering framework predicts how willing a customer is to purchase a mutual fund. Experimental results on a real-world data set from Taiwan Commercial bank demonstrated indicated that DECF approaches outperformed deep learning methods such as DCF and NCF. The proposed GraphDCF algorithm outperformed (by up to 2.3%) other frequently used approaches.
AB - Financial services are increasingly personalized due to the rapid development of artificial intelligence. In particular, precision marketing is the ultimate goal in providing financial services. However, precision marketing is hindered by problems in model scalability, cold starts, and insufficient transaction data points for many customers and products. Some researchers have attempted to solve these problems by incorporating deep learning into the collaborative filtering algorithm. This has improved the performance of the traditional matrix factorization algorithm, but it comes at the cost of imposing a limit on the capacity of the recommendation model to capture the complex interaction features. In this paper, we propose a graphical deep collaborative filtering (GraphDCF) algorithm for providing personalized mutual fund recommendations. The graph-structured network is constructed by connecting customer nodes with similar purchases and redeemed trading orders in the sequential view. In this manner, we can model different latent relationships among customers who have similar shopping habits. Subsequently, the corresponding embedding vector for each customer node is generated through an aggregate function based on similarity in transaction behaviors. Finally, to provide personalized recommendations, in addition to customer features and mutual fund attributes, the proposed deep embedded collaborative filtering framework predicts how willing a customer is to purchase a mutual fund. Experimental results on a real-world data set from Taiwan Commercial bank demonstrated indicated that DECF approaches outperformed deep learning methods such as DCF and NCF. The proposed GraphDCF algorithm outperformed (by up to 2.3%) other frequently used approaches.
KW - Deep collaborative filtering
KW - Graph neural network
KW - Mutual fund recommendation
KW - Node embedding
KW - Precision marketing
UR - http://www.scopus.com/inward/record.url?scp=85122155266&partnerID=8YFLogxK
U2 - 10.1016/j.eswa.2021.116311
DO - 10.1016/j.eswa.2021.116311
M3 - Article
AN - SCOPUS:85122155266
SN - 0957-4174
VL - 192
SP - 1
EP - 14
JO - Expert Systems with Applications
JF - Expert Systems with Applications
M1 - 116311
ER -