GAMMA: A Graph and Multi-view Memory Attention Mechanism for Top-N Heterogeneous Recommendation

Exploiting heterogeneous information networks (HIN) to top-N recommendation has been shown to alleviate the data sparsity problem present in recommendation systems. This requires careful effort in extracting relevant knowledge from HIN. However, existing models in this setting have the following shortcomings. Mainly, they are not end-to-end, which puts the burden on the system to first learn similarity or commuting matrix offline using some manually selected meta-paths before we train for the top-N recommendation objective. Further, they do not attentively extract user-specific information from HIN, which is essential for personalization. To address these challenges, we propose an end-to-end neural network model – GAMMA (Graph and Multi-view Memory Attention mechanism). We aim to replace the offline meta-path based similarity or commuting matrix computation with a graph attention mechanism. Besides, with different semantics of items in HIN, we propose a multi-view memory attention mechanism to learn more profound user-specific item views. Experiments on three real-world datasets demonstrate the effectiveness of our model for top-N recommendation setting.