Meta-learning enhanced next POI recommendation by leveraging check-ins
  from auxiliary cities
Jinze Wang Lu Zhang Zhu Sun Yew-Soon Ong
Most existing point-of-interest (POI) recommenders aim to capture user
preference by employing city-level user historical check-ins, thus facilitating
users' exploration of the city. However, the scarcity of city-level user
check-ins brings a significant challenge to user preference learning. Although
prior studies attempt to mitigate this challenge by exploiting various context
information, e.g., spatio-temporal information, they ignore to transfer the
knowledge (i.e., common behavioral pattern) from other relevant cities (i.e.,
auxiliary cities). In this paper, we investigate the effect of knowledge
distilled from auxiliary cities and thus propose a novel Meta-learning Enhanced
next POI Recommendation framework (MERec). The MERec leverages the correlation
of check-in behaviors among various cities into the meta-learning paradigm to
help infer user preference in the target city, by holding the principle of
"paying more attention to more correlated knowledge". Particularly, a
city-level correlation strategy is devised to attentively capture common
patterns among cities, so as to transfer more relevant knowledge from more
correlated cities. Extensive experiments verify the superiority of the proposed
MERec against state-of-the-art algorithms.