通过挖掘属性图规则提升推荐效果，解决信息缺失问题。

Recommendation systems are crucial in modern applications to enhance the user experience and drive business conversion rates through personalization. However, insufficient utilization of attribute information within the property graph remains a significant challenge. Most existing graph convolutional network (GCN) models do not consider attribute information, and those that do often employ a simplified triple format <users, items, attributes>, which fails to fully exploit the rich semantic structures of property graphs necessary for effective recommendations. To overcome these limitations, we introduce Rule-Driven Approach for Attribute Embedding (RAE), a novel methodology that enhances recommendation performance by effectively mining and utilizing semantic rules from property graphs. RAE applies a rule-mining process to extract meaningful rules that guide random walks in generating enriched attribute embeddings. These enriched embeddings are subsequently integrated into GCNs, surpassing conventional triple-based embedding techniques. We evaluate RAE on real-world datasets (e.g., Blogcatalog and Flickr) and demonstrate that RAE achieves an average improvement of 10.6% in both Recall@20 and NDCG@20 compared to state-of-the-art baselines, indicating superior relevance coverage and ranking rationality in top-20 recommendations. Additionally, RAE exhibits enhanced robustness against data sparsity and the attribute missingness problem. Our novel approach underscores the significant performance gains achieved in recommendation systems by fully leveraging attribute information within property graphs, enhancing both effectiveness and reliability.