HAIRec: A Hybrid Recommendation Framework Integrating Review Sentiment and Interaction Information

Recommender systems are widely used in e-commerce to model user preferences and deliver personalized services. However, conventional interaction-based recommendation approaches fail to fully capture users’ underlying motivations and emotional tendencies. User-generated reviews offer complementary sentiment information, yet integrating sentiment semantics with interaction modeling remains challenging due to feature heterogeneity and sparsity. This paper proposes a hybrid recommendation framework (HAIRec) that jointly models review sentiment and interaction information within a unified architecture. The model extracts sentiment representations, latent rating factors, user preference features, and item attributes through multi-source feature learning. To address the semantic heterogeneity between review sentiment and interaction behavior, a hierarchical heterogeneous feature fusion architecture is proposed. Specifically, a dual-level attention mechanism is designed to jointly model global feature importance and fine-grained semantic interactions, designed to facilitate multi-granularity preference learning across heterogeneous domains. In addition, a Deep & Cross Network V2 (DCN-V2) is adopted to explicitly and implicitly model high-order feature interactions. Extensive experiments are conducted on five real-world subcategories of the Amazon Product Dataset (including Movies and TV, Digital Music, Musical Instruments, Toys and Games, and Home and Kitchen). The experimental results demonstrate that the proposed HAIRec framework consistently outperforms several representative baselines (such as DeepCoNN, NARRE, and LightGCN). Notably, compared with the strong LightGCN benchmark, HAIRec achieves significant rating prediction improvements, reducing the Mean Squared Error (MSE) to 0.884 on Movies and TV, 0.882 on Digital Music, and 0.863 on Toys and Games, which corresponds to maximum performance gains of up to 7.14% in MSE reduction.