GETNext: Trajectory Flow Map Enhanced Transformer for Next POI Recommendation
This work addresses the problem of predicting users' future movements for recommendation systems, offering a novel approach that enhances accuracy and handles cold start, though it is incremental in combining existing techniques like transformers with new collaborative signals.
The paper tackles the next POI recommendation problem by proposing GETNext, a model that incorporates a global trajectory flow map and a Graph Enhanced Transformer to leverage collaborative signals from other users, achieving state-of-the-art performance with significant improvements and addressing cold start issues.
Next POI recommendation intends to forecast users' immediate future movements given their current status and historical information, yielding great values for both users and service providers. However, this problem is perceptibly complex because various data trends need to be considered together. This includes the spatial locations, temporal contexts, user's preferences, etc. Most existing studies view the next POI recommendation as a sequence prediction problem while omitting the collaborative signals from other users. Instead, we propose a user-agnostic global trajectory flow map and a novel Graph Enhanced Transformer model (GETNext) to better exploit the extensive collaborative signals for a more accurate next POI prediction, and alleviate the cold start problem in the meantime. GETNext incorporates the global transition patterns, user's general preference, spatio-temporal context, and time-aware category embeddings together into a transformer model to make the prediction of user's future moves. With this design, our model outperforms the state-of-the-art methods with a large margin and also sheds light on the cold start challenges within the spatio-temporal involved recommendation problems.