基于高斯过程与批量汤普森抽样的动态定价策略  

Dynamic Pricing Strategy Based on Gaussian Process and Parallel Thompson Sampling

作　　者：毕文杰[1] 王荣 BI Wenjie;WANG Rong(Business School,Central South University,Changsha 410083,China)

机构地区：[1]中南大学商学院,长沙410083

出　　处：《计算机工程与应用》2022年第16期303-311,共9页Computer Engineering and Applications

基　　金：国家自然科学基金重大研究计划(91646115)。

摘　　要：考虑短期内需求不确定情况下同类型产品的定价策略研究,引入高斯过程进行需求函数的学习,利用批量汤普森算法建立基于探索-利用的两阶段学习和决策过程的定价模型。在利用提出的GP-PTS(Gaussian process-parallel Thompson sampling)算法完成数值实验和某平台出行的真实数据应用后得出的结果表明:算法的精准度取决于特征是否完备,若给定一个先验且产品特征完备时,基于GP-PTS算法模拟出来的价格会取得比目前平台价格策略更好的收益,为企业在短期内进行定价决策提供良好借鉴。Considering the research on pricing strategies of the same type of products in the case of uncertain demand in the short term,this paper introduces Gaussian process to learn the demand function,and uses parallel Thompson algo-rithm to establish a two-stage learning and decision-making process pricing model based on exploration-exploitation trade-off.After using the proposed GP-PTS algorithm to complete the numerical experiment and the real data application,the results show that the accuracy of the algorithm depends on whether the features are complete.If a prior is given and the product features are complete,the price simulated by GP-PTS algorithm will obtain better benefits than the current plat-form pricing strategy,and will provide a good reference for enterprises to make pricing decisions in the short term.

关 键 词：动态定价 高斯过程 汤普森抽样 批量贝叶斯优化 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]