UAR-CFNet: Association Rule-Enhanced Cross-Domain Recommendation Under Data Sparsity Constraints

To effectively alleviate the common problems of information noise and information loss in personalized recommendation systems, as well as to address data sparsity and cold-start issues, this paper proposes a collaborative filtering recommendation model that integrates user attributes and association rules, named Impoved_UARCF. The model introduces a user attribute-sensitive module and a user-item rating-sensitive module to perform deep feature modeling from the perspectives of multi-dimensional user attributes and user-item rating interactions, respectively. The user attribute-sensitive module employs a similarity computation mechanism based on user attributes to mine and decouple deep attribute features among users, enhancing the discriminability and generalization ability of feature representations, thereby effectively resolving information noise and information loss. The user-item rating-sensitive module utilizes association rule mining technology to learn the relationship weights between users in real time, enabling accurate aggregation and propagation of user-item rating features, thus effectively addressing data sparsity and cold-start problems. Extensive experiments conducted on three public datasets verify the superiority of Impoved_UARCF in recommendation performance, as well as the effectiveness, scalability, and robustness of each module design.