Simple and Efficient Metapath Aggregated Network for Recommendation

Abstract

In the realm of graph-based recommendation algorithms, the concept of metapaths plays a pivotal role in capturing complex semantic relationships between various types of entities, thereby significantly enhancing the recommendation quality. However, existing metapath-based recommendation algorithms often struggle to fully utilize the rich information embedded in metapaths while maintaining conciseness. Certain methods employ intricate and time-intensive aggregation networks to extract information from metapaths, while other strategies, prioritizing efficiency, neglect to investigate metapath information thoroughly. To address the above issues, we propose a Simple and Efficient Metapath Aggregated Network (SEMAN) for Recommendation. We employ a metapath pre-aggregation strategy using simple average pooling and concatenation operators to ensure the overall efficiency of the model. Furthermore, to fully exploit metapath information, the complete pathway nodes, including intermediate nodes, and metapath structural information are separately represented as aggregated vectors. Finally, we have designed a metapath semantic fusion module based on a dual-layer attention mechanism to process the metapath pre-aggregated results from both user and item sides across different granular levels. The theoretical analysis of time complexity substantiates the high efficiency of our model. Extensive experiments on three real-world graph datasets show that our proposed model performs significantly better than the state-of-the-art baselines on both Recall and NDCG metrics.

Keywords

• Graph neural network
• metapath
• recommender systems