Recommending relevant items to users is a crucial task on online communities such as Reddit and Twitter. For recommendation system, representation learning presents a powerful technique that learns embeddings to represent user behaviors and capture item properties. However, learning embeddings on online communities is a challenging task because the user interest keep evolving. This evolution can be captured from 1) interaction between user and item, 2) influence from other users in the community. The existing dynamic embedding models only consider either of the factors to update user embeddings. However, at a given time, user interest evolves due to a combination of the two factors. To this end, we propose Influence-aware and Attention-based Co-evolutionary Network (IACN). Essentially, IACN consists of two key components: interaction modeling and influence modeling layer. The interaction modeling layer is responsible for updating the embedding of a user and an item when the user interacts with the item. The influence modeling layer captures the temporal excitation caused by interactions of other users. To integrate the signals obtained from the two layers, we design a novel fusion layer that effectively combines interaction-based and influence-based embeddings to predict final user embedding. Our model outperforms the existing state-of-the-art models from various domains.

中文翻译：

---

IACN：用于建议的基于影响力感知和基于注意力的协同进化网络

向用户推荐相关项目是Reddit和Twitter等在线社区的一项关键任务。对于推荐系统，表示学习提供了一种强大的技术，可以学习嵌入来表示用户行为并捕获项目属性。但是，由于用户兴趣不断发展，因此学习在线社区上的嵌入内容是一项艰巨的任务。可以从1）用户与项目之间的交互，2）来自社区中其他用户的影响中捕获这种演变。现有的动态嵌入模型仅考虑更新用户嵌入的任何因素。但是，在给定的时间，由于两个因素的结合，用户的兴趣在不断发展。为此，我们提出了基于影响力和注意力的协同进化网络（IACN）。IACN本质上包含两个关键组件：交互建模和影响建模层。交互建模层负责在用户与商品交互时更新用户和商品的嵌入。影响建模层捕获由其他用户的交互作用引起的时间激励。为了整合从这两层获得的信号，我们设计了一个新颖的融合层，该层有效地结合了基于交互的嵌入和基于影响的嵌入，以预测最终的用户嵌入。我们的模型优于各种领域的现有最新模型。为了整合从这两层获得的信号，我们设计了一个新颖的融合层，该层有效地结合了基于交互的嵌入和基于影响的嵌入，以预测最终的用户嵌入。我们的模型优于各种领域的现有最新模型。为了整合从这两层获得的信号，我们设计了一个新颖的融合层，该层有效地结合了基于交互的嵌入和基于影响的嵌入，以预测最终的用户嵌入。我们的模型优于各种领域的现有最新模型。