Department of Computing Science, University of Alberta, Edmonton, Canada.
Alberta Machine Intelligence Institute, Edmonton, Alberta, Canada.
PLoS One. 2021 Apr 29;16(4):e0249182. doi: 10.1371/journal.pone.0249182. eCollection 2021.
A consumer's "reservation price" (RP) is the highest price that s/he is willing to pay for one unit of a specified product or service. It is an essential concept in many applications, including personalized pricing, auction and negotiation. While consumers will not volunteer their RPs, we may be able to predict these values, based on each consumer's specific information, using a model learned from earlier consumer transactions. Here, we view each such (non)transaction as a censored observation, which motivates us to use techniques from survival analysis/prediction, to produce models that can generate a consumer-specific RP distribution, based on features of each new consumer. To validate this framework of RP, we run experiments on realistic data, with four survival prediction methods. These models performed very well (under three different criteria) on the task of estimating consumer-specific RP distributions, which shows that our RP framework can be effective.
消费者的“保留价格”（RP）是指他/她愿意为特定产品或服务的一个单位支付的最高价格。这是许多应用程序中的一个重要概念，包括个性化定价、拍卖和谈判。虽然消费者不会主动透露他们的 RP，但我们可以根据每个消费者的具体信息，使用从以前的消费者交易中学习到的模型来预测这些值。在这里，我们将每个此类（非）交易视为一个删失观测值，这促使我们使用生存分析/预测技术，根据每个新消费者的特征，生成消费者特定的 RP 分布模型。为了验证 RP 的这个框架，我们在真实数据上使用了四种生存预测方法进行实验。这些模型在估计消费者特定的 RP 分布的任务上表现非常出色（根据三个不同的标准），这表明我们的 RP 框架是有效的。
