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.
翻译:向用户推荐相关项目是Reddit 和 Twitter 等在线社群的一项重要任务。 对于建议系统来说, 代表式学习是一种强大的技术, 学习嵌入来代表用户的行为和捕捉项目属性。 但是, 学习嵌入到在线社群是一项艰巨的任务, 因为用户的兴趣在不断演变。 这一演变可以从1) 用户和项目之间的互动, 2) 来自社区中其他用户的影响。 现有的动态嵌入模型只考虑更新用户嵌入的因素之一。 但是, 在特定时间, 用户的兴趣会因两个因素的结合而演变。 为此, 我们建议了影响力和关注性共同进化网络(ICN) 。 从根本上说, IACN 由两个关键组成部分组成: 互动建模和影响建模层。 互动建模层负责更新用户的嵌入过程, 当用户与项目互动时, 一个项目则负责更新用户嵌入过程。 影响建模层捕捉到其他用户互动产生的时间刺激因素。 为了整合从两个层面获得的信号, 我们设计了一个新型的聚合层, 有效地将基于用户嵌入的模型和现有影响模型 嵌入到我们的各种嵌入模式 。