IACN：影响力感知和基于注意力的协同进化推荐网络
IACN: Influence-aware and Attention-based Co-evolutionary Network for Recommendation
摘要：
向用户推荐相关项目是Reddit和Twitter等在线社区的一项重要任务。对于推荐系统，表示学习提供了一种强大的技术，学习嵌入来表示用户行为和捕获项目属性。然而，学习嵌入在线社区是一项具有挑战性的任务，因为用户的兴趣不断变化。这种演变可以从1）用户和项目之间的交互，2）社区中其他用户的影响中捕获。现有的动态嵌入模型只考虑其中一个因素来更新用户嵌入。然而，在给定的时间，用户的兴趣演变由于这两个因素的组合。为此，我们提出了基于影响和注意力的协同进化网络（IACN）。本质上，IACN由两个关键组件组成：交互建模和影响建模层。交互建模层负责在用户与项交互时更新用户和项的嵌入。影响建模层捕获由其他用户的交互引起的时间激励。为了整合从两层获得的信号，我们设计了一个新的融合层，有效地结合了基于交互和基于影响的嵌入来预测最终的用户嵌入。我们的模型优于现有的国家的最先进的模型从各个领域。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
