Denoising Alignment with Large Language Model for Recommendation

作者：Yingtao Peng, Chen Gao, Yu Zhang, Tangpeng Dan, Xiaoyi Du, Hengliang Luo, Yong Li, Xiaofeng Meng · 发表于：ACM Transactions on Information Systems · 年份：2024 · DOI：10.1145/3696662 · 被引用次数：15 · 研究领域：Recommender Systems and Techniques、Image Retrieval and Classification Techniques、Topic Modeling

The mainstream approach of GNN-based recommendation aggregates high-order ID information associated with the node in the user-item graph. The aggregation pattern using ID as signal has two disadvantages: lack of textual semantics and the impact of interaction noise. These disadvantages pose a threat to effectively learn user preferences, especially in capturing intricate user-item semantic relationships. Although large language models (LLMs) allow the integration of rich textual information into recommenders and have had groundbreaking applications in recommender systems, current works need to bridge the gap between different representation spaces. This is because LLM-based methods align the representations of GNN-based models only by using text embedding of LLM, leading to unsatisfactory results. To address this challenge, we propose a denoising alignment framework with LLMs for GNN-based recommenders (DALR) , which aims to align structural representation with textual representation and mitigate the effects of noise. Specifically, we propose a modeling framework that integrates the representation of graph structure with textual information from LLMs to capture intricate user-item interactions. We also suggest an alignment paradigm to enhance representation performance by aligning semantic signals from LLMs and structural features from GNN models. Additionally, we introduce a contrastive learning scheme to relieve the impact of noise and improve model performance. Extensive e...