Latent Dirichlet Allocation for Internet Price War


  • Chenchen Li Shanghai Jiao Tong University
  • Xiang Yan Shanghai Jiao Tong University
  • Xiaotie Deng Shanghai Jiao Tong University
  • Yuan Qi Ant Financial Services Group
  • Wei Chu Ant Financial
  • Le Song Ant Financial Services Group
  • Junlong Qiao Ant Financial Services Group
  • Jianshan He Ant Financial Services Group
  • Junwu Xiong Ant Financial Services Group



Current Internet market makers are facing an intense competitive environment, where personalized price reductions or discounted coupons are provided by their peers to attract more customers. Much investment is spent to catch up with each other’s competitors but participants in such a price cut war are often incapable of winning due to their lack of information about others’ strategies or customers’ preference. We formalize the problem as a stochastic game with imperfect and incomplete information and develop a variant of Latent Dirichlet Allocation (LDA) to infer latent variables under the current market environment, which represents preferences of customers and strategies of competitors. Tests on simulated experiments and an open dataset for real data show that, by subsuming all available market information of the market maker’s competitors, our model exhibits a significant improvement for understanding the market environment and finding the best response strategies in the Internet price war. Our work marks the first successful learning method to infer latent information in the environment of price war by the LDA modeling, and sets an example for related competitive applications to follow.




How to Cite

Li, C., Yan, X., Deng, X., Qi, Y., Chu, W., Song, L., Qiao, J., He, J., & Xiong, J. (2019). Latent Dirichlet Allocation for Internet Price War. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 639-646.



AAAI Special Technical Track: AI for Social Impact