近日，实验室博士生吴有霖关于用户行为建模的研究被Cognitive Science Society（CogSci2025）录用。CogSci是认知科学领域的重要会议，被CCF推荐为B类国际学术会议。

标题：Clicking, Fast and Slow: Towards Intuitive and Analytical Behaviors Modeling for Recommender Systems

摘要：Recommender systems personalize content delivery based on users' interaction history. However, not all clicks result from deliberate decisions—many arise from intuitive reactions. Inspired by the dual process theory, we argue that intuitive clicks are primarily driven by System 1, reacting to superficial cues, while analytical clicks involve deeper processing by System 2, considering the semantic meaning and long-term preference. However, existing models overlook these cognitive mechanisms. To address this, we propose DualRec, a novel recommendation method that models both intuitive and analytical behaviors. DualRec encodes items using language models, leveraging shallow layers for superficial understanding (System 1) and deep layers for semantic comprehension (System 2). It employs Transformer-based encoders with two attention mechanisms to capture intuitive "fast" and analytical "slow" click patterns. A learnable fusion layer balances these behaviors. Extensive experiments demonstrate that DualRec outperforms existing methods and highlights the importance of integrating both cognitive processes in recommendations.

推荐系统根据用户的交互历史进行个性化内容推送。然而，并非所有的用户点击都源于深思熟虑--许多点击源于直觉。受双过程理论的启发，我们认为直觉点击主要由系统1驱动，是对表层线索的快速反馈，而分析性点击则由系统2主导，特别考虑了深层语义和长期偏好。然而，现有模型忽略了这些认知机制。为了解决这一问题，我们提出了DualRec，一种同时模拟直觉行为和分析行为的新推荐方法。DualRec使用语言模型对物品进行编码：利用LM的低层/高层分别表征浅层线索（系统1）/深层语义（系统2）。进而采用基于Transformer的编码器搭配两种注意力机制来捕捉快、慢两种点击模式，并辅以可学习的融合层来调和两种行为模式。实验证明，DualRec优于现有方法，并突出了在推荐系统中整合双过程认知理论的重要性。