论文标题
Uber-gnn：基于图形神经网络的基于用户的嵌入式建议
UBER-GNN: A User-Based Embeddings Recommendation based on Graph Neural Networks
论文作者
论文摘要
基于会话的建议问题旨在根据会话历史预测用户的下一个操作。先前的方法将历史记录模拟为序列，并通过RNN和GNN方法估算用户潜在特征，以提出建议。但是，在具有虚拟和真实商品的大规模且复杂的财务建议方案下，这种方法不足以代表准确的用户潜在功能并忽略用户的长期特征。为了考虑长期的偏好和动态利益，我们提出了一种新颖的方法，即使用图形神经网络，Uber-gnn提出的基于用户的嵌入式建议。 Uber-gnn利用结构化数据来生成长期的用户偏好，并将会话序列转移到图形中以生成基于图形的动态兴趣。然后将最终的用户潜在特征表示为使用注意机制的长期偏好和动态兴趣的组成。在真实ping上进行的广泛实验表明，Uber-GNN的表现优于基于最新会议的建议方法。
The problem of session-based recommendation aims to predict user next actions based on session histories. Previous methods models session histories into sequences and estimate user latent features by RNN and GNN methods to make recommendations. However under massive-scale and complicated financial recommendation scenarios with both virtual and real commodities , such methods are not sufficient to represent accurate user latent features and neglect the long-term characteristics of users. To take long-term preference and dynamic interests into account, we propose a novel method, i.e. User-Based Embeddings Recommendation with Graph Neural Network, UBER-GNN for brevity. UBER-GNN takes advantage of structured data to generate longterm user preferences, and transfers session sequences into graphs to generate graph-based dynamic interests. The final user latent feature is then represented as the composition of the long-term preferences and the dynamic interests using attention mechanism. Extensive experiments conducted on real Ping An scenario show that UBER-GNN outperforms the state-of-the-art session-based recommendation methods.
