.png){kind=icon}
.png){kind=icon}
Return
LLM-Enhanced Position-Aware Graph for Sequential Recommendation
.png){kind=icon}
PRE
AI
DOI：10.1109/tcss.2026.3696964.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Sequential recommendation aims to predict the next item that a user will interact with based on historical behavior sequences. In real-world scenarios, user-item interactions exhibit complex dependencies, which graph neural networks are well-suited to model by capturing high-order relationships between nodes. However, most existing graph-based sequential recommendation methods face two major challenges: 1) they often neglect positional information within sequences when constructing graphs; and 2) they suffer from noise introduced by accidental or unintended clicks. Recent advances in large language models (LLMs) offer a promising direction for mitigating these issues, due to their strong semantic understanding. However, directly leveraging LLMs may face task mismatch and excessive denoising may exacerbate the data sparsity. To this end, we propose an LLM-enhanced position-aware graph for sequential recommendation (LEPG4SR). Specifically, we design a position-aware item transition graph to model complex item relationships from a global perspective. We then utilize LLMs to extract semantic embeddings of item side information and filter out noisy data based on semantic similarity. To further combat data sparsity, we introduce a self-supervised learning strategy with a novel semantic perturbation-based data augmentation technique. Extensive experiments on three real-world datasets demonstrate that LEPG4SR can outperform the state-of-the-art sequential recommendation methods.
Keywords:
Graph neural network (GNN)
large language models (LLM)
self-supervised learning
sequential recommendation
Journal
IF:
4.9
Papers:
577
Citations:
6.8K