LLM-ESR: Large Language Models Enhancement for Long-tailed Sequential Recommendation

TL;DR

This paper introduces LLM-ESR, a framework that leverages semantic embeddings from large language models to improve long-tail user and item recommendations in sequential recommender systems, achieving superior results without extra inference costs.

Contribution

The paper proposes a novel LLM-based enhancement framework for sequential recommendation that addresses long-tail challenges through dual-view modeling and retrieval augmented self-distillation.

Findings

Outperforms existing baselines on three real-world datasets

Benefits long-tail users and items significantly

Demonstrates versatility across different SRS models

Abstract

Sequential recommender systems (SRS) aim to predict users' subsequent choices based on their historical interactions and have found applications in diverse fields such as e-commerce and social media. However, in real-world systems, most users interact with only a handful of items, while the majority of items are seldom consumed. These two issues, known as the long-tail user and long-tail item challenges, often pose difficulties for existing SRS. These challenges can adversely affect user experience and seller benefits, making them crucial to address. Though a few works have addressed the challenges, they still struggle with the seesaw or noisy issues due to the intrinsic scarcity of interactions. The advancements in large language models (LLMs) present a promising solution to these problems from a semantic perspective. As one of the pioneers in this field, we propose the Large Language Models Enhancement framework for Sequential Recommendation (LLM-ESR). This framework utilizes semantic embeddings derived from LLMs to enhance SRS without adding extra inference load from LLMs. To address the long-tail item challenge, we design a dual-view modeling framework that combines semantics from LLMs and collaborative signals from conventional SRS. For the long-tail user challenge, we propose a retrieval augmented self-distillation method to enhance user preference representation using more informative interactions from similar users. To verify the effectiveness and versatility of our proposed enhancement framework, we conduct extensive experiments on three real-world datasets using three popular SRS models. The results show that our method surpasses existing baselines consistently, and benefits long-tail users and items especially. The implementation code is available at https://github.com/Applied-Machine-Learning-Lab/LLM-ESR.