Abstract
In e-commerce, merchants usually increase their profit by issuing coupons to potential customers. If merchants don’t develop a suitable coupon strategy, or randomly issue coupons, they may not take effects and thus waste the budget. Therefore, it is very important for merchants to issue coupons to customers who are more likely to purchase, leading to the necessity of predicting consumer’s coupon usage. However, existing methods such as questionnaires cannot get enough data and traditional deep learning cannot solve the complex features of coupon usage prediction. To this end, this paper proposes a novel model for predicting customer’s coupon usage behavior with capsule network. It classifies coupon features into multiple groups of capsules, and designs two capsule network structures for predicting coupon usage behavior. Meanwhile, we intensively compare the proposed model with multi-layer perception (MLP), convolutional neural network (CNN) and recurrent neural network (RNN). The experimental results show that the proposed model has significantly better prediction accuracy (e.g. AUC).
Supported by NSFC grant 62172149, 61632009, 62172159, the Natural Science Foundation of Hunan Province of China (2021JJ30137).
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Jiang, W., Tan, Z., He, J., Zhang, J., Wang, T., Chen, S. (2022). Predicting Consumers’ Coupon-usage in E-commerce with Capsule Network. In: Lai, Y., Wang, T., Jiang, M., Xu, G., Liang, W., Castiglione, A. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2021. Lecture Notes in Computer Science(), vol 13156. Springer, Cham. https://doi.org/10.1007/978-3-030-95388-1_17
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