LDAG: Modeling Long-term interests by Directed Acyclic Graph Neural Network for Sequential Recommendation

Sequential recommendation aims to predict users' next interactions by analyzing their historical behaviour sequences. One common approach is modeling both long-term and short-term interests together, but there are still two major challenges that need to be addressed. The first challenge is that repeated learning of neighbor nodes in GNN will blur the long-term interests of users. The second challenge is that existing models often fuse a user's diverse interests together to predict their behaviour. However, each interest may independently affect the user's next interaction. In this work, we propose modeling long-term interests by Directed Acyclic Graph Neural Network(LDAG) to address the above challenges. Specifically, we use metric learning to transform the item sequence into a compact item-item directed acyclic graph. We also propose a directed acyclic graph convolution and a readout based on the sink set in DAG to aggregate and extract users' interests. Finally, users' long-term interests and diverse short-term interests are combined for the prediction of users' next behaviour. We conduct extensive experiments on three real-world datasets. Experimental results demonstrate that our proposed model outperforms current state-of-the-art methods. Further experiments illustrate the rationality and effectiveness of our proposed method.