Graph attention and text semantics improve personalized recommendation

With the increasing challenge of information overload, personalized recommendation systems play an essential role in delivering relevant content to users. Traditional collaborative filtering methods often suffer from data sparsity and cold-start problems, which limit their effectiveness in real-world applications. To address these issues, this paper proposes a personalized recommendation model that integrates graph attention mechanisms with auxiliary textual information. User-item interactions are modeled on a knowledge graph, where graph attention networks are employed to capture multi-hop user interests, while graph convolution is used to aggregate item-side neighborhood information. Textual data associated with users and items are encoded into semantic embeddings and incorporated to enrich the initial representations of graph entities. Experiments conducted on the Book-Crossing and MovieLens-1M datasets demonstrate that the proposed model achieves superior performance in terms of AUC, F1-score, and Top-K recall compared with several state-of-the-art baselines. The results indicate that combining graph-based modeling with textual semantic enhancement can effectively improve recommendation accuracy and robustness under sparse data conditions.