Enhancing Temporal Sensitivity of Large Language Model for Recommendation with Counterfactual Tuning

TL;DR

This paper introduces CETRec, a novel framework that enhances large language models' ability to understand and leverage temporal information in sequential recommendation tasks through counterfactual tuning based on causal inference.

Contribution

CETRec is the first approach to incorporate counterfactual causal inference for improving temporal sensitivity in LLM-based recommendation systems.

Findings

CETRec significantly improves recommendation accuracy on real-world datasets.

Counterfactual tuning enhances LLMs' understanding of temporal order and user preference evolution.

The framework demonstrates robustness across different datasets and settings.

Abstract

Recent advances have applied large language models (LLMs) to sequential recommendation, leveraging their pre-training knowledge and reasoning capabilities to provide more personalized user experiences. However, existing LLM-based methods fail to sufficiently leverage the rich temporal information inherent in users' historical interaction sequences, stemming from fundamental architectural constraints: LLMs process information through self-attention mechanisms that lack inherent sequence ordering and rely on position embeddings designed primarily for natural language rather than user interaction sequences. This limitation significantly impairs their ability to capture the evolution of user preferences over time and predict future interests accurately. To address this critical gap, we propose \underline{C}ounterfactual \underline{E}nhanced \underline{T}emporal Framework for LLM-Based \underline{Rec}ommendation (CETRec). CETRec is grounded in causal inference principles, which allow it to isolate and measure the specific impact of temporal information on recommendation outcomes. Combined with our counterfactual tuning task derived from causal analysis, CETRec effectively enhances LLMs' awareness of both absolute order (how recently items were interacted with) and relative order (the sequential relationships between items). Extensive experiments on real-world datasets demonstrate the effectiveness of our CETRec. Our code is available at https://anonymous.4open.science/r/CETRec-B9CE/.