Improve Temporal Awareness of LLMs for Sequential Recommendation

TL;DR

This paper introduces a prompting framework to enhance large language models' ability to recognize and utilize temporal information, significantly improving their performance in sequential recommendation tasks without additional training.

Contribution

The paper proposes three novel prompting strategies inspired by human cognition to improve LLMs' temporal awareness in sequential recommendation tasks.

Findings

Significant performance improvement on MovieLens-1M and Amazon Review datasets.

Enhanced zero-shot capabilities in sequential recommendation tasks.

Effective aggregation of divergent LLM ranking results.

Abstract

Large language models (LLMs) have demonstrated impressive zero-shot abilities in solving a wide range of general-purpose tasks. However, it is empirically found that LLMs fall short in recognizing and utilizing temporal information, rendering poor performance in tasks that require an understanding of sequential data, such as sequential recommendation. In this paper, we aim to improve temporal awareness of LLMs by designing a principled prompting framework inspired by human cognitive processes. Specifically, we propose three prompting strategies to exploit temporal information within historical interactions for LLM-based sequential recommendation. Besides, we emulate divergent thinking by aggregating LLM ranking results derived from these strategies. Evaluations on MovieLens-1M and Amazon Review datasets indicate that our proposed method significantly enhances the zero-shot capabilities of LLMs in sequential recommendation tasks.