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.

翻译：下一兴趣点推荐旨在根据用户当前状态和历史信息预测其近期未来动向，对用户和服务提供商均具有重要价值。然而，该问题极为复杂，需综合考量多种数据趋势，包括空间位置、时间上下文和用户偏好等。现有研究大多将下一兴趣点推荐视为序列预测问题，忽视了来自其他用户的协同信号。为此，我们提出一种与用户无关的全局轨迹流图及新型图增强Transformer模型（GETNext），以更充分地利用广泛的协同信号实现更精准的下一兴趣点预测，同时缓解冷启动问题。GETNext将全局转移模式、用户一般偏好、时空上下文以及时间感知类别嵌入共同整合到Transformer模型中，用于预测用户未来动向。通过这一设计，我们的模型以显著优势超越当前最优方法，并有望解决涉及时空信息的推荐系统中的冷启动难题。