论文标题
在线POI建议：在流中学习动态的地球人类互动
Online POI Recommendation: Learning Dynamic Geo-Human Interactions in Streams
论文作者
论文摘要
在本文中，我们重点介绍了在流中为在线POI推荐建模动态地球人类相互作用的问题。具体而言，我们将媒介中的地球互动建模问题提出为新颖的深层交互式增强学习框架，在该框架中，代理是推荐的，而动作是下一个要访问的POI。我们将强化学习环境独特地建模为用户和地理空间环境（POI，POI类别，功能区域）的联合组成和连接的组成。用户在流中访问POI的事件更新了用户和地理空间环境的状态；代理商认为更新的环境状态以提出在线建议。具体而言，我们通过将所有用户，访问和地理空间上下文统一为动态知识图流来对混合用户事件流进行建模，以模拟人类，地球人类，地理乔治相互作用。我们设计了一种解决过期信息挑战的退出机制，设计了一种元路径方法来应对推荐候选生成挑战，并开发了一种新的深层政策网络结构来应对不同的行动空间挑战，最后提出了一种有效的对抗性培训方法以优化。最后，我们提出了广泛的实验，以证明方法的增强性能。
In this paper, we focus on the problem of modeling dynamic geo-human interactions in streams for online POI recommendations. Specifically, we formulate the in-stream geo-human interaction modeling problem into a novel deep interactive reinforcement learning framework, where an agent is a recommender and an action is a next POI to visit. We uniquely model the reinforcement learning environment as a joint and connected composition of users and geospatial contexts (POIs, POI categories, functional zones). An event that a user visits a POI in stream updates the states of both users and geospatial contexts; the agent perceives the updated environment state to make online recommendations. Specifically, we model a mixed-user event stream by unifying all users, visits, and geospatial contexts as a dynamic knowledge graph stream, in order to model human-human, geo-human, geo-geo interactions. We design an exit mechanism to address the expired information challenge, devise a meta-path method to address the recommendation candidate generation challenge, and develop a new deep policy network structure to address the varying action space challenge, and, finally, propose an effective adversarial training method for optimization. Finally, we present extensive experiments to demonstrate the enhanced performance of our method.
