  Point-of-Interest (POI) recommender systems play a vital role in people's
lives by recommending unexplored POIs to users and have drawn extensive
attention from both academia and industry. Despite their value, however, they
still suffer from the challenges of capturing complicated user preferences and
fine-grained user-POI relationship for spatio-temporal sensitive POI
recommendation. Existing recommendation algorithms, including both shallow and
deep approaches, usually embed the visiting records of a user into a single
latent vector to model user preferences: this has limited power of
representation and interpretability. In this paper, we propose a novel
topic-enhanced memory network (TEMN), a deep architecture to integrate the
topic model and memory network capitalising on the strengths of both the global
structure of latent patterns and local neighbourhood-based features in a
nonlinear fashion. We further incorporate a geographical module to exploit
user-specific spatial preference and POI-specific spatial influence to enhance
recommendations. The proposed unified hybrid model is widely applicable to
various POI recommendation scenarios. Extensive experiments on real-world
WeChat datasets demonstrate its effectiveness (improvement ratio of 3.25% and
29.95% for context-aware and sequential recommendation, respectively). Also,
qualitative analysis of the attention weights and topic modeling provides
insight into the model's recommendation process and results.
