Users often face bundle promotions when purchasing, where they have to select between two options: buy the single item at full price, or buy the bundle at a discount. In this scenario, users' preferences are usually influenced by the projection bias, that is, users often believe that their future preferences are similar to their current preferences, causing them to make irrational and short-sighted decisions. It is of great significance to analyze the effect of the projection bias on users' preferences, and this study may help understand users' decision-making process and provide bundling and pricing strategies for sellers. Prior works typically use a linear bias model for qualitative analysis, and they cannot quantitatively calculate users' nonlinear and personalized bias. In this work, we propose Pobe, a projection bias-embedded preference model to accurately predict users' choices. The proposed Pobe introduces the prospect theory to analyze users' irrational decisions, and utilizes the weight function to handle users' nonlinear and personalized bias. Based on the proposed Pobe, we also study the impact of items' correlations or discount prices on users' choices, and provide four bundling strategies. Experimental results show that the proposed method can achieve better performance than prior works, especially when only small data is available.

翻译：用户在购买时经常面临捆绑促销，需在两种选项间做出选择：以原价购买单件商品，或以折扣价购买捆绑商品。在此情境下，用户的偏好往往受到投射偏差的影响，即用户通常认为其未来偏好与当前偏好相似，导致其做出非理性且短视的决策。分析投射偏差对用户偏好的影响具有重要意义，该研究有助于理解用户的决策过程，并为卖家提供捆绑与定价策略。已有研究通常采用线性偏差模型进行定性分析，无法定量计算用户的非线性和个性化偏差。本文提出Pobe——一种嵌入投射偏差的偏好模型，用于准确预测用户的选择。所提出的Pobe引入前景理论分析用户的非理性决策，并利用权重函数处理用户的非线性和个性化偏差。基于所提出的Pobe，我们进一步研究了商品相关性或折扣价格对用户选择的影响，并提供了四种捆绑策略。实验结果表明，所提出的方法在性能上优于先前工作，尤其是在仅有少量数据可用的情况下。