@article{Xie_Li_Lui_2019, title={Optimizing Discount and Reputation Trade-Offs in E-Commerce Systems: Characterization and Online Learning}, volume={33}, url={https://ojs.aaai.org/index.php/AAAI/article/view/4800}, DOI={10.1609/aaai.v33i01.33017992}, abstractNote={<p>Feedback-based reputation systems are widely deployed in E-commerce systems. Evidences showed that earning a reputable label (for sellers of such systems) may take a substantial amount of time and this implies a reduction of profit. We propose to enhance sellersâ€™ reputation via price discounts. However, the challenges are: <em>(1) The demands from buyers depend on both the discount and reputation; (2) The demands are unknown to the seller.</em> To address these challenges, we first formulate a profit maximization problem via a semiMarkov decision process (SMDP) to explore the optimal trade-offs in selecting price discounts. We prove the monotonicity of the optimal profit and optimal discount. Based on the monotonicity, we design a QLFP (Q-learning with forward projection) algorithm, which infers the optimal discount from historical transaction data. We conduct experiments on a dataset from to show that our QLFP algorithm improves the profit by as high as 50% over both the classical Q-learning and speedy Q-learning algorithm. Our QLFP algorithm also improves the profit by as high as four times over the case of not providing any price discount.</p>}, number={01}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Xie, Hong and Li, Yongkun and Lui, John C. S.}, year={2019}, month={Jul.}, pages={7992-7999} }