  Out-of-town recommendation is designed for those users who leave their
home-town areas and visit the areas they have never been to before. It is
challenging to recommend Point-of-Interests (POIs) for out-of-town users since
the out-of-town check-in behavior is determined by not only the user's
home-town preference but also the user's travel intention. Besides, the user's
travel intentions are complex and dynamic, which leads to big difficulties in
understanding such intentions precisely. In this paper, we propose a
TRAvel-INtention-aware Out-of-town Recommendation framework, named TRAINOR. The
proposed TRAINOR framework distinguishes itself from existing out-of-town
recommenders in three aspects. First, graph neural networks are explored to
represent users' home-town check-in preference and geographical constraints in
out-of-town check-in behaviors. Second, a user-specific travel intention is
formulated as an aggregation combining home-town preference and generic travel
intention together, where the generic travel intention is regarded as a mixture
of inherent intentions that can be learned by Neural Topic Model (NTM). Third,
a non-linear mapping function, as well as a matrix factorization method, are
employed to transfer users' home-town preference and estimate out-of-town POI's
representation, respectively. Extensive experiments on real-world data sets
validate the effectiveness of the TRAINOR framework. Moreover, the learned
travel intention can deliver meaningful explanations for understanding a user's
travel purposes.
