  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.
