Abstract
Customer purchase behavior segmentation plays an important role in the modern economy. We proposed a Bayesian non-parametric (BNP)-based framework, named Simultaneous Customer Segmentation and Utility Discovery (UtSeg), to discover customer segmentation without knowing specific forms of utility functions and parameters. For the segmentation based on BNP models, the unknown type of functions is usually modeled as a non-homogeneous point process (NHPP) for each mixture component. However, the inference of these models is complex and time-consuming. To reduce such complexity, traditionally, economists will use one specific utility function in a heuristic way to simplify the inference. We proposed to automatically select among multiple utility functions instead of searching in a continuous space. We further unified the parameters for different types of utility functions with the same prior distribution to improve efficiency. We tested our model with synthetic data and applied the framework to real-supermarket data with different products, and showed that our results can be interpreted with common knowledge.
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Notes
- 1.
For a Gamma distribution, we simplify both actual parameters into one parameter.
- 2.
This can be determined by the loss used. We use the quadratic loss, but Gaussian distribution is used to approximate the Chi-square distribution when data volume is large.
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Zhang, S., Luo, L., Li, Z., Wang, Y., Chen, F., Xu, R. (2020). Simultaneous Customer Segmentation and Behavior Discovery. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Communications in Computer and Information Science, vol 1332. Springer, Cham. https://doi.org/10.1007/978-3-030-63820-7_14
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