CSRec: Rethinking Sequential Recommendation from A Causal Perspective

TL;DR

Causal Sequential Recommendation, a novel formulation of sequential recommendation that distinguishes between a user's natural preference and their actual purchasing decision, and can be seamlessly integrated into existing next-prediction-based methodologies.

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.

Figures (1)

• Figure 1: Causal Graph for Sequential Recommendation.

Theorems & Definitions (6)

• Definition 2.1: Recommendation in Causal Inference
• Theorem 4.1
• Remark 4.2
• Definition 5.1: Binary Average Treatment Effect(ATE)
• Definition 5.2: Treatment Effect in Recommendation
• Remark 6.1