  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.
