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.

该论文提出一种名为 DGCF 的动态图协同过滤框架，利用动态图来同时捕捉项目和用户的协作和顺序关系，以解决传统的序列模型无法准确捕捉协作信息的问题，并在实验中表现出优越的性能。

动态图协同过滤

Dynamic Graph Collaborative Filtering

Classical collaborative filtering, and content-based filtering methods try to
learn a static recommendation model given training data. These approaches are
far from ideal in highly dynamic recommendation domains such as news
recommendation and computational advertisement, where the set of items and
users is very fluid. In this work, we investigate an adaptive clustering
technique for content recommendation based on exploration-exploitation
strategies in contextual multi-armed bandit settings. Our algorithm takes into
account the collaborative effects that arise due to the interaction of the
users with the items, by dynamically grouping users based on the items under
consideration and, at the same time, grouping items based on the similarity of
the clusterings induced over the users. The resulting algorithm thus takes
advantage of preference patterns in the data in a way akin to collaborative
filtering methods. We provide an empirical analysis on medium-size real-world
datasets, showing scalability and increased prediction performance (as measured
by click-through rate) over state-of-the-art methods for clustering bandits. We
also provide a regret analysis within a standard linear stochastic noise
setting.

本文提出了一种自适应聚类技术，以探索 - 利用策略为基础，用于内容推荐系统中高动态性的推荐领域，该算法利用数据中的喜好模式，具有与协同过滤方法类似的优点，对中等规模的真实世界数据集进行了实证分析，表现出可扩展性和预测性能提高。