时空邻域感知的时序兴趣点推荐
TP391; 如何捕捉用户行为的动态变化和依赖关系是当前兴趣点推荐的一个重要问题,主要面临着数据稀疏、时空序列特征提取难以及用户个性化差异不易捕捉等挑战.为了解决这些挑战,提出了一种基于时空邻域感知及隐含状态变化的时序兴趣点推荐方法.该方法将用户行为的学习转换成了潜在状态的学习,并以一种结合距离信息的方式引入空间信息,有效地捕捉了用户的移动特征.首先,利用变分自编码器表征用户的潜在状态,再通过图神经网络学习到潜在状态之间的依赖关系,从而捕捉到用户行为的时序依赖;然后,利用注意力机制和径向基函数来捕捉用户与地点候选集之间的空间依赖,进而评估用户访问每个地点的概率,实现兴趣点推荐.在三个真实数据集...
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| Published in | 计算机科学与探索 Vol. 18; no. 7; pp. 1865 - 1878 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | Chinese |
| Published |
广东工业大学 计算机学院,广州 510006%广东工业大学 计算机学院,广州 510006
01.07.2024
汕头大学,广东 汕头 515000 |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1673-9418 |
| DOI | 10.3778/j.issn.1673-9418.2305113 |
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| Abstract | TP391; 如何捕捉用户行为的动态变化和依赖关系是当前兴趣点推荐的一个重要问题,主要面临着数据稀疏、时空序列特征提取难以及用户个性化差异不易捕捉等挑战.为了解决这些挑战,提出了一种基于时空邻域感知及隐含状态变化的时序兴趣点推荐方法.该方法将用户行为的学习转换成了潜在状态的学习,并以一种结合距离信息的方式引入空间信息,有效地捕捉了用户的移动特征.首先,利用变分自编码器表征用户的潜在状态,再通过图神经网络学习到潜在状态之间的依赖关系,从而捕捉到用户行为的时序依赖;然后,利用注意力机制和径向基函数来捕捉用户与地点候选集之间的空间依赖,进而评估用户访问每个地点的概率,实现兴趣点推荐.在三个真实数据集上进行了实验比较和分析,显示了该方法相比于现有的基准算法具有更好的时序推荐性能. |
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| AbstractList | TP391; 如何捕捉用户行为的动态变化和依赖关系是当前兴趣点推荐的一个重要问题,主要面临着数据稀疏、时空序列特征提取难以及用户个性化差异不易捕捉等挑战.为了解决这些挑战,提出了一种基于时空邻域感知及隐含状态变化的时序兴趣点推荐方法.该方法将用户行为的学习转换成了潜在状态的学习,并以一种结合距离信息的方式引入空间信息,有效地捕捉了用户的移动特征.首先,利用变分自编码器表征用户的潜在状态,再通过图神经网络学习到潜在状态之间的依赖关系,从而捕捉到用户行为的时序依赖;然后,利用注意力机制和径向基函数来捕捉用户与地点候选集之间的空间依赖,进而评估用户访问每个地点的概率,实现兴趣点推荐.在三个真实数据集上进行了实验比较和分析,显示了该方法相比于现有的基准算法具有更好的时序推荐性能. |
| Abstract_FL | How to capture the dynamic changes and dependencies of user behavior is a vital issue existing in point-of-interest(POI)recommendation.It mainly faces challenges including data scarcity,difficulty in extracting spatio-temporal sequence features and in capturing users·individuated differences.In order to address these challenges,this paper proposes a time-sequence POI recommendation method based on spatio-temporal vicinity perception and implicit changes of users·state.This method is aimed at converting the learning of user behavior into the learning of users·latent state,combined with distance information to introduce spatial information,which effectively captures users·mobile characteristics.Firstly,the variational autoencoder is utilized to represent the potential state of users.And then the dependency among the latent states is learnt through the graph neural network so as to capture the time-sequence dependence of user behavior.Furthermore,this paper makes use of the attention mechanism and radial ba-sis function to capture the spatial dependence between the user and location candidate sets.Next,this paper evalu-ates the frequencies of user visiting each location,hence achieving point-of-interest recommendation.Experimental comparison and analysis on three real datasets demonstrate that the temporal recommendation performance of the proposed method is superior to existing benchmark algorithms. |
| Author | 蔡瑞初 邓峰颖 温雯 梁方宇 郝志峰 |
| AuthorAffiliation | 广东工业大学 计算机学院,广州 510006%广东工业大学 计算机学院,广州 510006;汕头大学,广东 汕头 515000 |
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| Author_FL | CAI Ruichu HAO Zhifeng DENG Fengying WEN Wen LIANG Fangyu |
| Author_FL_xml | – sequence: 1 fullname: WEN Wen – sequence: 2 fullname: DENG Fengying – sequence: 3 fullname: HAO Zhifeng – sequence: 4 fullname: CAI Ruichu – sequence: 5 fullname: LIANG Fangyu |
| Author_xml | – sequence: 1 fullname: 温雯 – sequence: 2 fullname: 邓峰颖 – sequence: 3 fullname: 郝志峰 – sequence: 4 fullname: 蔡瑞初 – sequence: 5 fullname: 梁方宇 |
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| ClassificationCodes | TP391 |
| ContentType | Journal Article |
| Copyright | Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
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| DocumentTitle_FL | Recommendation Method for Time-Sequence Point of Interest via Spatio-Temporal Vicinity Perception |
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| Keywords | 注意力机制 point-of-interest(POI)recommendation variational auto-encoder 图神经网络 attention mechanism 变分自编码器 兴趣点推荐 graph neural network |
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| Publisher | 广东工业大学 计算机学院,广州 510006%广东工业大学 计算机学院,广州 510006 汕头大学,广东 汕头 515000 |
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| Title | 时空邻域感知的时序兴趣点推荐 |
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