Probe：学习跨期选择中用户的个性化投射偏差
Probe: Learning Users' Personalized Projection Bias in Intertemporal Choices
AI总结：
本文提出偏好模型 Probe，结合投射偏差与参考点效应，利用前景理论建模用户跨期选择中的捆绑购买决策，并通过理论与实验证明其优于现有方法。
摘要：
跨期选择涉及需要在当前成本与未来收益之间进行权衡的决策。一种特定类型的跨期选择是在购买单个商品与选择包含该商品的捆绑包之间做出决定。以往研究假设个体对这些选择所涉及的因素具有准确的预期。然而在现实中，用户对这些因素的感知往往存在偏差，从而导致非理性和次优的决策。在本工作中，我们特别关注两种常见的偏差：投射偏差和参考点效应。为了解决这些偏差，我们提出了一种新颖的嵌入偏差的偏好模型，称为 Probe。Probe 引入了一个权重函数来捕捉用户的投射偏差，以及一个价值函数来解释参考点效应，并引入行为经济学中的前景理论来结合权重函数和价值函数。这使我们能够确定用户选择捆绑包或单个商品的概率。我们提供了充分的理论分析，以证明投射偏差对捆绑销售策略设计的影响。通过实验结果，我们表明所提出的 Probe 模型优于现有方法，并有助于更好地理解用户在捆绑购买中的非理性行为。这项研究可以促进对用户决策机制的更深入理解，支持提供个性化服务，并帮助用户做出更理性和最优的决策。
英文摘要：
Intertemporal choices involve making decisions that require weighing the costs in the present against the benefits in the future. One specific type of intertemporal choice is the decision between purchasing an individual item or opting for a bundle that includes that item. Previous research assumes that individuals have accurate expectations of the factors involved in these choices. However, in reality, users' perceptions of these factors are often biased, leading to irrational and suboptimal decision-making. In this work, we specifically focus on two commonly observed biases: projection bias and the reference-point effect. To address these biases, we propose a novel bias-embedded preference model called Probe. The Probe incorporates a weight function to capture users' projection bias and a value function to account for the reference-point effect, and introduce prospect theory from behavioral economics to combine the weight and value functions. This allows us to determine the probability of users selecting the bundle or a single item. We provide a thorough theoretical analysis to demonstrate the impact of projection bias on the design of bundle sales strategies. Through experimental results, we show that the proposed Probe model outperforms existing methods and contributes to a better understanding of users' irrational behaviors in bundle purchases. This investigation can facilitate a deeper comprehension of users' decision-making mechanisms, enable the provision of personalized services, and assist users in making more rational and optimal decisions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
