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DAlim: Machine learning based intelligent lucky money determination for large-scale E-commerce businesses
Fu M.1; Wong C.M.3; Zhu H.1; Huang Y.1; Li Y.1; Zheng X.2; Wu J.2; Yang J.2; Vong C.M.3
Conference Name16th International Conference on Service-Oriented Computing (ICSOC 2018)
Source PublicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11236 LNCS
Conference DateNov 12-15, 2018
Conference PlaceHangzhou, Zhejiang, China

E-commerce businesses compete in the market by conducting marketing strategies consisting of four aspects: customers, products, marketplaces and intermediaries. One of the widely-used marketing strategies, called Lucky Money, is capable of encouraging customers to buy products from marketplaces. However, the amount of luck money for each customer is usually randomly determined or even manually determined and cannot fully achieve the business objectives. This paper proposes a machine-learning based lucky money determination approach, called DAliM, for e-commerce businesses to achieve their desired goals. We implement DAliM for the “Double 11 Global Shopping Festival 2017” initiated by Alibaba Group and evaluate it using a few hundred million real customers from all over the world. The experimental results demonstrate that our method manages to decrease the lucky money spent by 41.71% and increase the final purchase rate by 24.94% compared to the state-of-the-art baseline.

KeywordData Mining E-commerce Lucky Money Machine Learning Price Optimization Price Prediction
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Document TypeConference paper
Affiliation1.Alibaba Group
2.Macquarie University
3.Universidade de Macau
Recommended Citation
GB/T 7714
Fu M.,Wong C.M.,Zhu H.,et al. DAlim: Machine learning based intelligent lucky money determination for large-scale E-commerce businesses[C],2018:740-755.
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