Modeling behavior sequence for personalized fund recommendation with graphical deep collaborative filtering

Yi Ching Chou, Chiao Ting Chen, Szu-Hao Huang*

*此作品的通信作者

研究成果: Article同行評審

摘要

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.

原文English
文章編號116311
頁(從 - 到)1-14
頁數14
期刊Expert Systems with Applications
192
DOIs
出版狀態Published - 15 4月 2022

指紋

深入研究「Modeling behavior sequence for personalized fund recommendation with graphical deep collaborative filtering」主題。共同形成了獨特的指紋。

引用此