下一个兴趣点推荐(next POI recommendation)作为基于位置社交网络的主要应用之一,为用户和服务提供商带来了显著的实用价值.现有的POI推荐模型主要依赖于目标用户的历史签到数据进行推荐,没有充分利用其他用户移动轨迹数据的潜在价值,也未有效提取和融合时空-类别信息的特征.为了解决上述问题,提出了一种融合人群移动轨迹和时空-类别的下一个兴趣点推荐模型(GGCN-STC).依据用户的移动轨迹构建区域轨迹图,提出了门控图卷积神经网络对共同移动轨迹进行建模;将签到序列中的时空-类别信息进行多维度的特征融合;利用自注意力机制捕获用户偏好,为用户提供更准确的POI推荐.在两个真实数据集上进行实验比较与分析,结果表明该模型优于其他模型.
Next Point of Interest Recommendation Fusing Crowd Movement Trajectories and Spatiotemporal-Category Features
Next point of interest(POI)recommendation,as one of the main applications of location-based social net-works,brings significant practical value to both users and service providers.However,existing POI recommendation mod-els mainly rely on the historical check-in data of target users,without fully leveraging the potential value of movement tra-jectories data from others,nor effectively extracting and fusing spatiotemporal-category features.To address these issues,this paper proposes GGCN-STC,a next POI recommendation model that integrates crowd movement trajectories and spa-tiotemporal-category features.Firstly,a region trajectory graph is constructed based on user movement trajectories,and the gated graph convolutional neural network is proposed to construct a model of shared graph.Secondly,spatiotemporal-category information from check-in sequences is fused into multi-dimensional features.Finally,it provides more accurate POI recommendations for users by capturing their preferences with self-attention mechanisms.Experiments are conducted for comparison and analysis on two real datasets,and the results demonstrate that the proposed model outperforms others.
POI recommendationgated graph convolutional neural networkself-attentionspatiotemporal network
万方数据
维普
