CSRec: Rethinking Sequential Recommendation from A Causal Perspective

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.Although 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, called Causal Sequential Recommendation.Instead of merely predicting the next item in a sequence, CSRec distinguishes between a user's natural preference and their actual purchasing decision.It predicts both aspects within a sequential context and traces how current decisions are formed and causally influenced by various factors.Applying such a causal framework can isolate the impact of recommender systems on user decisions, thereby opening new avenues for evaluation and design.This includes assessing how different strategies influence users' trust in the system and determining the optimal recommender system to maximize advertising benefits.CSRec can be seamlessly integrated into existing next-prediction-based methodologies.Experimental evaluations on both synthetic and real-world datasets demonstrate that the proposed implementation significantly improves upon state-of-the-art baselines.[code can be accessed here].