计算机科学
编码器
变压器
互联网
机器学习
人工智能
期限(时间)
人机交互
数据挖掘
万维网
工程类
量子力学
操作系统
电气工程
物理
电压
作者
Chaoqun Wang,Yuhan Dong,Kai Zhang
标识
DOI:10.1109/dsit55514.2022.9943896
摘要
With the widespread adoption of mobile devices and Internet technologies, location-based social networks (LBSNs) provide a multitude of services to people. Next point of interest (POI) recommendation has become an important problem. The purpose of this task is to discover the location history activities from the user's preferences and recommend the next POI. The researchers utilized RNN model or attention mechanism to integrate long- and short-term interests and achieve success. However, existing works manually designed feature interaction to fuse different preference, or shallowly mined spatio-temporal information. To address the limitations, we propose an transformer-based model named Long- and Short-term Preference Learning with Enhanced Spatial Transformer(LSEST). Our model adopts a unified model to simultaneously model long-term and short-term preferences, so that the two user preferences can be interacted deeply to represent a comprehensive user preference. In addition, our model utilizes two transformer encoders to capture the temporal and spatial dependencies, respectively, and enhances the spatio-temporal consistency greatly. Extensive experiment on two real-world check-in datasets shows the superiority of our model compared to the state-of-the-art methods.
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