  Sequential recommendation systems (SRS) serve the purpose of predicting
users' subsequent preferences based on their past interactions and have been
applied across various domains such as e-commerce and social networking
platforms. However, practical SRS encounters challenges due to the fact that
most users engage with only a limited number of items, while the majority of
items are seldom consumed. These challenges, termed as the long-tail user and
long-tail item dilemmas, often create obstacles for traditional SRS methods.
Mitigating these challenges is crucial as they can significantly impact user
satisfaction and business profitability. While some research endeavors have
alleviated these issues, they still grapple with issues such as seesaw or noise
stemming from the scarcity of interactions. The emergence of large language
models (LLMs) presents a promising avenue to address these challenges from a
semantic standpoint. In this study, we introduce the Large Language Models
Enhancement framework for Sequential Recommendation (LLM-ESR), which leverages
semantic embeddings from LLMs to enhance SRS performance without increasing
computational overhead. To combat the long-tail item challenge, we propose a
dual-view modeling approach that fuses semantic information from LLMs with
collaborative signals from traditional SRS. To address the long-tail user
challenge, we introduce a retrieval augmented self-distillation technique to
refine user preference representations by incorporating richer interaction data
from similar users. Through comprehensive experiments conducted on three
authentic datasets using three widely used SRS models, our proposed enhancement
framework demonstrates superior performance compared to existing methodologies.
