Lei, Y., Shen, L., Sun, Z., He, T., Feng, S., & Liu, G. (2025). COSTA: Contrastive Spatial and Temporal Debiasing framework for next POI recommendation. Neural Networks, 185, 107212. https://doi.org/10.1016/j.neunet.2025.107212
Abstract:
Current research on next point-of-interest (POI) recommendation focuses on capturing users’ behavior patterns residing in their mobility trajectories. However, the learning process will inevitably cause discrepancies between the recommendation and individuals’ spatial and temporal preferences, and consequently lead to specific biases in the next POI recommendation, namely the spatial bias and temporal bias. This work, for the first time, reveals the existence of such spatial and temporal biases and explores their detrimental impact on user experiences via in-depth data analysis. To mitigate the spatial and temporal biases, we propose a novel Contrastive Spatial and Temporal Debiasing framework for the next POI recommendation (COSTA). COSTA enhances spatial–temporal signals from both the user and POI sides through the user- and location-side spatial–temporal signal encoders. Based on these enhanced representations, it utilizes contrastive learning to strengthen the alignment between user representations and suitable POI representations, while distinguishing them from mismatched POI representations. Furthermore, we introduce two novel metrics, Discounted Spatial Cumulative Gain (DSCG) and Discounted Temporal Cumulative Gain (DTCG), to quantify the severity of spatial and temporal biases. Extensive experiments conducted on three real-world datasets demonstrate that COSTA significantly outperforms state-of-the-art next POI recommendation approaches in terms of debiasing metrics without compromising recommendation accuracy.
License type:
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Funding Info:
There was no specific funding for the research done