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.

通过元学习框架以及相关性策略，研究了从其他城市中提取的知识对用户偏好预测的影响，提出了一个新的元学习增强下一个感兴趣点（POI）推荐框架（MERec），并通过实验证实了其优越性。