GETNext: Trajectory Flow Map Enhanced Transformer for Next POI Recommendation

Abstract

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.

arXiv commentThis work is published on SIGIR22

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• LINE: Large-scale Information Network Embedding
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Cited by in corpus (10)

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• Bi-Level Graph Structure Learning for Next POI Recommendation
• TrajGPT: Controlled Synthetic Trajectory Generation Using a Multitask Transformer-Based Spatiotemporal Model
• CoMaPOI: A Collaborative Multi-Agent Framework for Next POI Prediction Bridging the Gap Between Trajectory and Language
• Where have you been? A Study of Privacy Risk for Point-of-Interest Recommendation
• Spatial-Temporal Cross-View Contrastive Pre-training for Check-in Sequence Representation Learning
• Analyzing and Mitigating Repetitions in Trip Recommendation