从因果视角重构推荐系统，区分用户本意与实际购买决策。
CSRec: Rethinking Sequential Recommendation from A Causal Perspective
- 用因果建模分离用户自然偏好与推荐影响
- 在真实和合成数据上优于现有基线方法
- 适合想改进推荐系统可解释性的研究者
序列推荐系统的核心在于理解用户决策过程。现有方法大多基于历史购买记录进行序列预测，虽能捕捉用户自然偏好，却难以准确建模实际推荐场景，尤其无法量化失败推荐对后续购买的影响。此外，推荐系统本身对用户决策的影响未被有效分离与量化。为此，我们提出一种新范式——因果序列推荐（CSRec）。不同于传统预测下一个物品，CSRec旨在预测推荐物品在序列上下文中的接受概率，并回溯当前决策形成机制。关键在于，它能有效隔离影响用户最终决策的各类因素，尤其是推荐系统本身的干预作用，为推荐系统设计开辟新路径。该方法可无缝融入现有框架。在合成与真实数据集上的实验表明，其性能显著优于当前最优基线。
原文摘要 · Abstract (English)
The essence of sequential recommender systems (RecSys) lies in understanding how users make decisions. Most existing approaches frame the task as sequential prediction based on users' historical purchase records. While effective in capturing users' natural preferences, this formulation falls short in accurately modeling actual recommendation scenarios, particularly in accounting for how unsuccessful recommendations influence future purchases. Furthermore, the impact of the RecSys itself on users' decisions has not been appropriately isolated and quantitatively analyzed. To address these challenges, we propose a novel formulation of sequential recommendation, termed Causal Sequential Recommendation (CSRec). Instead of predicting the next item in the sequence, CSRec aims to predict the probability of a recommended item's acceptance within a sequential context and backtrack how current decisions are made. Critically, CSRec facilitates the isolation of various factors that affect users' final decisions, especially the influence of the recommender system itself, thereby opening new avenues for the design of recommender systems. CSRec can be seamlessly integrated into existing methodologies. Experimental evaluations on both synthetic and real-world datasets demonstrate that the proposed implementation significantly improves upon state-of-the-art baselines.
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