CSRec: Rethinking Sequential Recommendation from A Causal Perspective

TL;DR

CSRec introduces a causal perspective to sequential recommendation, focusing on modeling how recommendations influence user decisions and isolating the system's effect, leading to improved recommendation accuracy.

Contribution

The paper proposes a novel causal formulation for sequential recommendation that isolates the influence of the recommender system and enhances prediction accuracy.

Findings

Significant improvement over state-of-the-art baselines

Effective in modeling recommendation influence on user decisions

Validated on synthetic and real-world datasets

Abstract

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.