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

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ID: 282790
2023
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Abstract
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.
Reference Key
ong2023metalearning Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Jinze Wang; Lu Zhang; Zhu Sun; Yew-Soon Ong
Journal arXiv
Year 2023
DOI
DOI not found
URL
http://arxiv.org/abs/2308.09309v1
Keywords
cs.lg cs.ir

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