论文标题
图形神经网络具有动态和静态表示形式以供社会推荐
Graph Neural Networks with Dynamic and Static Representations for Social Recommendation
论文作者
论文摘要
基于图形神经网络的推荐系统由于其出色地学习包括社交网络在内的各种附带信息的能力而获得了越来越多的研究兴趣。但是，以前的工作通常专注于建模用户，对项目没有太多关注。此外，随着时间的推移，项目吸引的可能变化，就像用户的动态兴趣很少被考虑，并且项目之间的相关性也不一样。为了克服这些局限性，本文提出了具有动态和静态表示的社会推荐（GNN-DSR）的图形神经网络，该网络既考虑用户和项目的动态和静态表示形式，并结合其关系影响。 GNN-DSR分别对用户兴趣和项目吸引力的短期动态和长期静态交互表示进行建模。此外，注意机制用于汇总用户对目标用户的社会影响以及相关项目对给定项目的影响。将用户和项目的最终潜在因素合并为预测。在三个现实世界推荐系统数据集上进行的实验验证了GNN-DSR的有效性。
Recommender systems based on graph neural networks receive increasing research interest due to their excellent ability to learn a variety of side information including social networks. However, previous works usually focus on modeling users, not much attention is paid to items. Moreover, the possible changes in the attraction of items over time, which is like the dynamic interest of users are rarely considered, and neither do the correlations among items. To overcome these limitations, this paper proposes graph neural networks with dynamic and static representations for social recommendation (GNN-DSR), which considers both dynamic and static representations of users and items and incorporates their relational influence. GNN-DSR models the short-term dynamic and long-term static interactional representations of the user's interest and the item's attraction, respectively. Furthermore, the attention mechanism is used to aggregate the social influence of users on the target user and the correlative items' influence on a given item. The final latent factors of user and item are combined to make a prediction. Experiments on three real-world recommender system datasets validate the effectiveness of GNN-DSR.
