论文标题
通过多关系建模授权下一个POI建议
Empowering Next POI Recommendation with Multi-Relational Modeling
论文作者
论文摘要
随着移动设备和Web应用程序的广泛采用，基于位置的社交网络（LBSN）提供了与位置相关的个人级别的活动和体验。下一个利益点（POI）建议是LBSN中最重要的任务之一，旨在通过从用户的历史活动中发现偏好来对用户进行个性化建议。值得注意的是，LBSN提供了无与伦比的访问，可以访问有关用户和POI的丰富的异质关系信息（包括用户 - 用户社会关系，例如家庭或同事；以及用户poi访问关系）。这种关系信息具有促进下一个POI建议的巨大潜力。但是，大多数现有方法要么仅关注用户访问，要么基于过度简化的假设处理不同的关系，同时忽略了关系异质性。为了填补这些关键空隙，我们提出了一个新型框架备忘录，该备忘录有效地利用了与多网络表示模块的异质关系，并明确地将偶然的用户poi相互影响与耦合的再现神经网络结合在一起。对现实世界LBSN数据的广泛实验验证了我们框架优于最先进的下一个POI推荐方法。
With the wide adoption of mobile devices and web applications, location-based social networks (LBSNs) offer large-scale individual-level location-related activities and experiences. Next point-of-interest (POI) recommendation is one of the most important tasks in LBSNs, aiming to make personalized recommendations of next suitable locations to users by discovering preferences from users' historical activities. Noticeably, LBSNs have offered unparalleled access to abundant heterogeneous relational information about users and POIs (including user-user social relations, such as families or colleagues; and user-POI visiting relations). Such relational information holds great potential to facilitate the next POI recommendation. However, most existing methods either focus on merely the user-POI visits, or handle different relations based on over-simplified assumptions while neglecting relational heterogeneities. To fill these critical voids, we propose a novel framework, MEMO, which effectively utilizes the heterogeneous relations with a multi-network representation learning module, and explicitly incorporates the inter-temporal user-POI mutual influence with the coupled recurrent neural networks. Extensive experiments on real-world LBSN data validate the superiority of our framework over the state-of-the-art next POI recommendation methods.
