Meta-Learning Enhanced Neural ODE for Citywide Next POI Recommendation

Recommending citywide POIs where users would visit in the next future benefits many location-based businesses and individuals. To train a decent recommendation model, adequate historical data is usually a prerequisite. However, historical check-ins are usually distributed unevenly, which leads to many cities suffering from data scarcity. To make matters worse, transferring knowledge from data sufficient cities is challenging due to the varying distribution of POIs and city structures. Most of existing next POI recommendation methods assume that the training data is adequate and can not solve these problems. In this paper, we propose a novel meta-learning enhanced neural ordinary differential equation (ODE) method, namely METAODE, which models city-invariant information and city-specified information separately to achieve accurate citywide next POI recommendation. For transferring knowledge from data sufficient cities, METAODE learns city-invariant information including the representation of POIs categories and user groups to extract user preference. Basing on that, METAODE employs a GRU-ODE-Bayes model for city-specified information modeling. It can not only capture the sequential relationships within the historical check-ins but also model the irregular-sampled timestamp in the continuous timeline. Moreover, METAODE leverages meta-learning mechanism to optimize the parameters on various data sufficient cities and train a well-generalized initialization, which can be effectively adapted to data insufficient cities to enhance recommendation performance. Extensive experiments on real-world datasets demonstrate the effectiveness of METAODE. Comparing with the state-of-the-art baselines, METAODE achieves 6.21% and 14.77% improvements on HR and NDCG, respectively.