Dynamic recommendation is essential for modern recommender systems to provide real-time predictions based on sequential data. In real-world scenarios, the popularity of items and interests of users change over time. Based on this assumption, many previous works focus on interaction sequences and learn evolutionary embeddings of users and items. However, we argue that sequence-based models are not able to capture collaborative information among users and items directly. Here we propose Dynamic Graph Collaborative Filtering (DGCF), a novel framework leveraging dynamic graphs to capture collaborative and sequential relations of both items and users at the same time. We propose three update mechanisms: zero-order 'inheritance', first-order 'propagation', and second-order 'aggregation', to represent the impact on a user or item when a new interaction occurs. Based on them, we update related user and item embeddings simultaneously when interactions occur in turn, and then use the latest embeddings to make recommendations. Extensive experiments conducted on three public datasets show that DGCF significantly outperforms the state-of-the-art dynamic recommendation methods up to 30. Our approach achieves higher performance when the dataset contains less action repetition, indicating the effectiveness of integrating dynamic collaborative information.

中文翻译：

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动态图协同过滤

动态推荐对于现代推荐系统基于顺序数据提供实时预测至关重要。在现实情况下，项目的受欢迎程度和用户的兴趣会随着时间而变化。基于此假设，许多先前的工作都集中在交互序列上，并学习用户和项目的进化嵌入。但是，我们认为基于序列的模型不能直接捕获用户和项目之间的协作信息。在这里，我们提出了动态图协作过滤（DGCF），这是一种利用动态图来同时捕获项目和用户的协作和顺序关系的新颖框架。我们提出了三种更新机制：零阶“继承”，一阶“传播”和二阶“聚合”，表示发生新的互动时对用户或物品的影响。基于它们，当交互依次发生时，我们会同时更新相关的用户和项目嵌入，然后使用最新的嵌入提出建议。在三个公共数据集上进行的广泛实验表明，DGCF的性能明显优于最先进的动态推荐方法（最多30个）。当数据集包含较少的动作重复时，我们的方法将获得更高的性能，从而表明集成动态协作信息的有效性。