Rule-Assisted Attribute Embedding

Recommendation systems often overlook the rich attribute information embedded in property graphs, limiting their effectiveness. Existing graph convolutional network (GCN) models either ignore attributes or rely on simplistic <user, item, attribute> triples, failing to capture deeper semantic structures. We propose RAE (Rule- Assisted Approach for Attribute Embedding), a novel method that improves recommendations by mining semantic rules from property graphs to guide attribute embedding. RAE performs rule-based random walks to generate enriched attribute representations, which are integrated into GCNs. Experiments on real-world datasets (BlogCatalog, Flickr) show that RAE outperforms state-of-the-art baselines by 10.6% on average in Recall@20 and NDCG@20. RAE also demonstrates greater robustness to sparse data and missing attributes, highlighting the value of leveraging structured attribute information in recommendation tasks.