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Most existing point-of-interest (POI) recommenders aim to capture userpreference by employing city-level user historical check-ins, thus facilitatingusers' exploration of the city. However, the scarcity of city-level usercheck-ins brings a significant challenge to user preference learning. Althoughprior studies attempt to mitigate this challenge by exploiting various contextinformation, e.g., spatio-temporal information, they ignore to transfer theknowledge (i.e., common behavioral pattern) from other relevant cities (i.e.,auxiliary cities). In this paper, we investigate the effect of knowledgedistilled from auxiliary cities and thus propose a novel Meta-learning Enhancednext POI Recommendation framework (MERec). The MERec leverages the correlationof check-in behaviors among various cities into the meta-learning paradigm tohelp infer user preference in the target city, by holding the principle of"paying more attention to more correlated knowledge". Particularly, acity-level correlation strategy is devised to attentively capture commonpatterns among cities, so as to transfer more relevant knowledge from morecorrelated cities. Extensive experiments verify the superiority of the proposedMERec against state-of-the-art algorithms.

Meta-learning enhanced next POI recommendation by leveraging check-ins from auxiliary cities