Abstract
Robo-advisor is a type of financial recommendation that can provide investors with financial advice or investment management online. Data clustering and item recommendation are both important and challenging in Robo-advisor. These two tasks are often considered independently and most efforts have been made to tackle them separately. However, users in data clustering and group relationship in item recommendation are inherently related. For example, a large number of financial transactions include not only the user’s asset information, but also the user’s social information. The existence of relations between users and groups motivates us to jointly perform clustering and item recommendation for Robo-advisor in this paper. In particular, we provide a principle way to capture the relations between users and groups, and propose a novel framework CLURE, which fuses data CLUstering and item REcommendation into a coherent model. With experiments on benchmark and real-world datasets, we demonstrate that the proposed framework CLURE achieves superior performance on both tasks compared to the state-of-the-art methods.
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Acknowledgments
This work was supported by the National Key R&D Program of China 2018YFB1003203 and the Natural Science Foundation of China (Grant No. 61672528, 61773392, 61702539).
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Xue, J., Zhu, E., Liu, Q., Wang, C., Yin, J. (2018). A Joint Approach to Data Clustering and Robo-Advisor. In: Sun, X., Pan, Z., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2018. Lecture Notes in Computer Science(), vol 11063. Springer, Cham. https://doi.org/10.1007/978-3-030-00006-6_9
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