Abstract
With the rapid development of mobile payment technology in China, people can use smartphone with some mobile payment apps (such as Alipay, WeChat pay and Apple pay etc.) to pay bills instead of paying cash. Some commercial platforms accumulated large transaction date from users’ smartphones. In the repeat consumption activities, the final few (or recency) consumption has a great impact on current consumption than long-ago consumption. But traditional HMM can’t deal with this recency effect in our repeat consumption case. This paper proposes a modified HMM method based on recency effect to predict the users’ repeat consumption behavior. We introduce a factor to represent the different recency effect of different time distance. An empirical study on real-world data sets shows encouraging results on our approach, especially on the consumer group which has the most uncertain consumption behavior.
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Acknowledgment
This work is supported by Zhejiang Provincial Natural Science Foundation of China (No. LY17F020008).
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Zheng, Z., Zhou, Y., Sun, L. (2019). A Recency Effect Hidden Markov Model for Repeat Consumption Behavior Prediction. In: Li, S. (eds) Green, Pervasive, and Cloud Computing. GPC 2018. Lecture Notes in Computer Science(), vol 11204. Springer, Cham. https://doi.org/10.1007/978-3-030-15093-8_31
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DOI: https://doi.org/10.1007/978-3-030-15093-8_31
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