DCRLRec: Dual-domain contrastive reinforcement large language model for recommendation

Abstract

Graph Neural Networks (GNN) possess powerful relational modeling capabilities, making them a leading solution for capturing and understanding users’ latent preferences, thereby significantly advancing personalized recommendation systems. However, current GNN-based recommendation methods heavily depend on explicit and static data, which results in overlooking the semantic value of user and item information. This oversight can lead to biases in understanding user preferences thus degrade recommendation performance. To address this problem, we propose a novel Dual-domain Contrastive Reinforcement Large Language Model for Recommendation (DCRLRec), which leverages large language models (LLMs) to perform inference across both the textual and graph domains, while applying contrastive reinforcement to enhance the alignment and representation of user and item nodes for personalized recommendations. The DCRLRec includes three key modules: collaborative domain feature perception module, semantic graph domain reinforcement module and contrastive alignment module. Dual-domain information leverages the advanced reasoning capabilities of the LLM to augment the user interaction features of the collaborative domain and the semantic graph domain to capture complex semantic and structural information about items to produce different representations for users and items, respectively. Furthermore, a cross-domain contrastive reinforcement method is introduced to align embeddings from both domains, ensuring high-quality user recommendations. Through experiments on two benchmark datasets, compared with the state-of-the-art baselines, the extensive results exhibit that DCRLRec achieves competitive improvements of up to 3.61% in AUC and 2.21% in F1 scores, respectively.