User behaviors on an e-commerce app not only contain different kinds of feedback on items but also sometimes imply the cognitive clue of the user's decision-making. For understanding the psychological procedure behind user decisions, we present the behavior path and propose to match the user's current behavior path with historical behavior paths to predict user behaviors on the app. Further, we design a deep neural network for behavior path matching and solve three difficulties in modeling behavior paths: sparsity, noise interference, and accurate matching of behavior paths. In particular, we leverage contrastive learning to augment user behavior paths, provide behavior path self-activation to alleviate the effect of noise, and adopt a two-level matching mechanism to identify the most appropriate candidate. Our model shows excellent performance on two real-world datasets, outperforming the state-of-the-art CTR model. Moreover, our model has been deployed on the Meituan food delivery platform and has accumulated 1.6% improvement in CTR and 1.8% improvement in advertising revenue.

翻译：用户在电商应用中的行为不仅包含对物品的不同反馈，有时还暗示着用户决策的认知线索。为了理解用户决策背后的心理过程，我们提出了行为路径的概念，并主张将用户当前行为路径与历史行为路径进行匹配，以预测用户在应用中的行为。进一步地，我们设计了一种用于行为路径匹配的深度神经网络，解决了行为路径建模中的三个难点：稀疏性、噪声干扰以及行为路径的精确匹配。具体而言，我们利用对比学习增强用户行为路径，通过行为路径自激活机制减轻噪声影响，并采用两级匹配机制来识别最合适的候选对象。我们的模型在两个真实数据集上表现出色，超越了当前最优的点击率预测模型。此外，该模型已在美团外卖平台部署，实现了点击率提升1.6%、广告收入提升1.8%的累积效果。