Disentangled Graph Debiasing for Next POI Recommendation

Graph neural networks play a pivotal role in various location-based applications, showcasing their compelling ability to capture collaborative signals across user check-in sequences. Recent advancements in next POI recommendation have further leveraged spatio-temporal graphs to uncover the transitional and geographical regularities. However, these methods are usually vulnerable due to the presence of data biases in real-life scenarios, which may mislead the model to disproportionately favoring certain POIs. To this end, this paper proposes a new graph debiasing paradigm for POI recommendation, which disentangles causal and bias knowledge within spatio-temporal graphs, allowing for not only the mitigation of bias issues, but also the utilization of causal information from spatial and temporal perspectives. Specifically, to facilitate graph debiasing at its topological level, an adaptive edge mask generator is first designed to explicitly decompose an entangled graph into causal and bias subgraphs. We encourage the stable relationships between the causal subgraph and the prediction, while the bias subgraph targets at the skewed bias distribution. We further enhance the independence between such two parts by employing a causal-bias disagreement regularization to encourage their distribution in separate semantic spaces. In addition, an inter-view contrastive learning module is also applied to maintain the relation discriminability of transitional and geographical representations. Extensive experiments on three real-world datasets demonstrate the superiority of our proposed model on recommendation performance, as well as its robustness against data bias.