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

TL;DR

This paper introduces MERec, a meta-learning framework that leverages check-in data from auxiliary cities to improve next POI recommendation in data-scarce target cities, by transferring relevant behavioral patterns.

Contribution

The paper proposes a novel meta-learning approach that transfers knowledge from auxiliary cities based on city-level correlations to enhance POI recommendation accuracy.

Findings

MERec outperforms state-of-the-art algorithms in experiments.

City-level correlation strategy effectively captures relevant patterns.

Knowledge transfer from auxiliary cities improves user preference inference.

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.