Multi-Relational Hierarchical Attention for Top-k Recommendation

As one of the critical application directions in the Recommendation Systems domain, the top-k recommendation model is to rank all candidate items through non-explicit feedback (e.g., some implicit interact behavior, like clicking, collecting, or viewing) from users. In this ranking, the rank shows the users' satisfaction with recommended items or the relevance of the target item. Although previous methods all improve the performance of the final recommended ranking, they suffer from several limitations. To overcome these limitations, we propose a Multi-Relational Hierarchical Attention within Graph Neural Network (GNN)-attention-Deep Neural Network (DNN) architecture for the topk recommendation, named MRHA for brevity. In our proposed method, we combine the GNN's ability to learn the local item representation of graph-structure data and attention-DNN architecture's ability to learn the user's preference. For processing the multi-relational data that occurs in the real application scenarios, we propose a novel hierarchical attention mechanism based on the GNN-attention-DNN architecture. The comparative experiments conducted on two real-world representative datasets show the effectiveness of the proposed method.