Enhancing POI Recommendation through Global Graph Disentanglement with POI Weighted Module

TL;DR

This paper introduces GDPW, a novel POI recommendation framework that jointly models POI categories, time, and various weighting factors using graph-based disentanglement, leading to improved prediction accuracy.

Contribution

It proposes a graph disentanglement approach with weighted modules to better capture POI category and time relationships for enhanced recommendations.

Findings

GDPW outperforms existing models by 3% to 11% in experiments.

The framework effectively models POI category-time relationships.

Weighted transition and distance factors improve recommendation accuracy.

Abstract

Next point of interest (POI) recommendation primarily predicts future activities based on users' past check-in data and current status, providing significant value to users and service providers. We observed that the popular check-in times for different POI categories vary. For example, coffee shops are crowded in the afternoon because people like to have coffee to refresh after meals, while bars are busy late at night. However, existing methods rarely explore the relationship between POI categories and time, which may result in the model being unable to fully learn users' tendencies to visit certain POI categories at different times. Additionally, existing methods for modeling time information often convert it into time embeddings or calculate the time interval and incorporate it into the model, making it difficult to capture the continuity of time. Finally, during POI prediction, various weighting information is often ignored, such as the popularity of each POI, the transition relationships between POIs, and the distances between POIs, leading to suboptimal performance. To address these issues, this paper proposes a novel next POI recommendation framework called Graph Disentangler with POI Weighted Module (GDPW). This framework aims to jointly consider POI category information and multiple POI weighting factors. Specifically, the proposed GDPW learns category and time representations through the Global Category Graph and the Global Category-Time Graph. Then, we disentangle category and time information through contrastive learning. After prediction, the final POI recommendation for users is obtained by weighting the prediction results based on the transition weights and distance relationships between POIs. We conducted experiments on two real-world datasets, and the results demonstrate that the proposed GDPW outperforms other existing models, improving performance by 3% to 11%.