Abstract: Review-based hybrid recommendation systems explore the semantic aspects of user preferences by integrating user reviews into collaborative filtering methods.However,these methods ignore the semantic gap between the semantic reviews and user interactions,and thus insufficiently exploit the inherent similarities among them.On the other hand,most existing hybrid re-commendations deeply couple collaborative signals with semantic reviews,which fails to model recommendations in a fine-grained manner.To overcome these limitations,this paper proposes a collaborative-content semantic fusion for review-enhanced recommendation(CCSFR) model.The model leverages the similarity between collaborative signals and review semantics to learn more comprehensive user interests and item features,and to enhance the embedded representations of users and items.To fully leverage the similarities between collaborative signals and review semantics,a hybrid content representation learning module is designed to decouple collaborative filtering and content-based recommendation,modelling user interactions and review semantics as interaction graphs and aspect graphs,respectively.Subsequently,a graph convolutional neural network is utilized to capture both collaborative signals and semantic features to learn embedded representations of users and items in both collaborative and content modes.Building on this,a collaborative semantic fusion strategy is employed to obtain unified and high-quality representations by integrating heterogeneous features.This strategy unifies the collaborative and content modes in a shared semantic space through semantic alignment,thereby reducing the semantic gap between them.Furthermore,graph contrastive learning is adopted to capture the similarity between the two modes by optimizing the mutual information between both modes and promoting complementary integration to enhance the quality of the representation.Experiments on three datasets show that CCSFR improves average 8.5% and 5.8% on nDCG compared to the optimal baseline,and 8.2% and 4.6% on recall metrics.The results of these experiments fully demonstrate that the collaborative semantic fusion strategy can effectively integrate collaborative signals and review semantics to generate more discriminative personalized representations,thereby improving recommendation performance.

Key words: Recommendation systems, Contrastive learning, Graph neural network, Semantic alignment, Heterogeneous fusion