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Article(id=1146098720635982590, tenantId=1146029695717560320, journalId=1146031591421210625, issueId=1146500022012580582, articleNumber=null, orderNo=22, doi=10.3981/j.issn.1000-7857.2024.01.00099, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1704384000000, receivedDateStr=2024-01-05, revisedDate=1721318400000, revisedDateStr=2024-07-19, acceptedDate=1744646400000, acceptedDateStr=2025-04-15, onlineDate=1751180828484, onlineDateStr=2025-06-29, pubDate=1747065600000, pubDateStr=2025-05-13, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1749744000000, onlineIssueDateStr=2025-06-13, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=1751180828484, onlineFirstDateStr=2025-06-29, sourceXml=null, magXml=null, createTime=1751180828484, creator=18627231156, updateTime=1774079668604, updator=sys-migrate, issue=Issue{id=1146500022012580582, tenantId=1146029695717560320, journalId=1146031591421210625, year='2025', volume='43', issue='9', pageStart='1', pageEnd='100', issueExtLink='null', onlineDate='null', pubDate='1747065600000', pubDateStr='2025-05-13', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1751276506188, creator='13701087609', updateTime=1774330384082, updator='13041195026', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1243194994346013325, tenantId=1146029695717560320, journalId=1146031591421210625, issueId=1146500022012580582, language=EN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1243194994350207630, tenantId=1146029695717560320, journalId=1146031591421210625, issueId=1146500022012580582, language=CN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=76, endPage=83, ext={EN=ArticleExt(id=1146098722032685848, articleId=1146098720635982590, tenantId=1146029695717560320, journalId=1146031591421210625, language=EN, title=Research progress on point-of-interest recommendation methods based on graph neural networks, columnId=1150494644690366681, journalTitle=Science & Technology Review, columnName=Papers, runingTitle=null, highlight=null, articleAbstract=

Cross-discipline is the growth point of new science and the inevitable trend of scientific development. The important application of location-based service, point−of−interest (POI) recommendation, as a research topic at the intersection of computer science and geographic information science, it plays an important role in promoting the cross−fertilization research of the two disciplines in the spatio−temporal data analysis and other related fields. This paper analyzes the influencing factors of POI recommendation, focuses on POI recommendation methods based on graph neural networks, including recommendation based on graph attention network, graph convolution network, and graph auto−encoder, with a comparative analysis of their respective characteristics; discusses some key challenges encountered in POI recommendation, such as data sparsity, cold start issues, and user dynamic preference issues, and proposes potential solution ideas to these challenges, makes prospects for the future development trend of POI recommendation.

, authors=null, authorsList=Jinfeng FANG, Zuyi CHEN, authorCompany=null, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=All rights reserved. Unauthorized reproduction is prohibited., copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1146098724100477823, articleId=1146098720635982590, tenantId=1146029695717560320, journalId=1146031591421210625, language=CN, title=基于图神经网络的兴趣点推荐方法研究进展, columnId=1146540929516700224, journalTitle=科技导报, columnName=研究论文, runingTitle=null, highlight=null, articleAbstract=

交叉学科是新科学的生长点，是科学发展的必然趋势。基于位置服务的重要应用——兴趣点推荐，作为计算机学科与地理信息学科相交叉的研究课题，对于推动2个学科在时空数据分析等相关领域的交叉融合研究具有重要作用。分析了兴趣点推荐的影响因素即地理位置、时间因素、社交关系和流行度，重点阐述了基于图神经网络的兴趣点推荐方法，包括基于图注意力网络、图卷积网络、图自编码器的推荐，并对其特点进行对比；讨论了在兴趣点推荐中存在的一些关键挑战，如数据稀疏性、冷启动问题和用户动态偏好问题，并针对各项挑战提出相应的解决思路，提出了结合多种影响因素的推荐，跨领域推荐以及动态偏好推荐的发展趋势。

, authors=

方金凤，讲师，研究方向为时空大数据、兴趣点推荐、轨迹预测等，电子信箱：

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方金凤，讲师，研究方向为时空大数据、兴趣点推荐、轨迹预测等，电子信箱：

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方金凤，讲师，研究方向为时空大数据、兴趣点推荐、轨迹预测等，电子信箱：

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IEEE Transactions on Big Data, 2025, 1- 16., articleTitle=A multiplex hyper-graph attribute-based graph collaborative filtering for cold-start POI recommendation, refAbstract=null), Reference(id=1242143434732024501, tenantId=1146029695717560320, journalId=1146031591421210625, articleId=1146098720635982590, doi=10.1016/j.knosys.2022.108632, pmid=null, pmcid=null, year=2022, volume=245, issue=null, pageStart=108632, pageEnd=null, url=null, language=null, rfNumber=50, rfOrder=49, authorNames=Bayram F, Ahmed B S, Kassler A, journalName=Knowledge-Based Systems, refType=null, unstructuredReference= Bayram F , Ahmed B S , Kassler A . From concept drift to model degradation: An overview on performance-aware drift detectors[J]. Knowledge-Based Systems, 2022, 245: 108632., articleTitle=From concept drift to model degradation: An overview on performance-aware drift detectors, refAbstract=null), Reference(id=1242143434811716278, tenantId=1146029695717560320, journalId=1146031591421210625, articleId=1146098720635982590, doi=null, pmid=null, pmcid=null, year=2024, volume=11, issue=1, pageStart=46, pageEnd=null, url=null, language=null, rfNumber=51, rfOrder=50, authorNames=Fan X Y, Ji Y Q, Hui B, journalName=Complex & Intelligent Systems, refType=null, unstructuredReference= Fan X Y , Ji Y Q , Hui B . A dynamic preference recommen-dation model based on spatiotemporal knowledge graphs[J]. 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类别 代表算法 主要技术 说明 优点
基于图注意力网络的推荐 IAGNN[15] GAT 利用分层注意力网络来捕捉用户对POI的全局和局部偏好 更好地捕捉用户意图，实现个性化推荐
MGAN[16] GAT 利用图注意力网络从多张特征图中学习POI表示 在统一模型中从多方面学习用户偏好
GE2AT[17] GAT+multi−layer perceptron 将注意力机制引入二分图中共同学习用户偏好特征 充分考虑用户的社会影响力，有效缓解数据稀疏性问题
SLS−REC[19] GAT+dynamic propagation mechanism 利用注意力机制捕获用户与POI的时间演化 融合时间因素，缓解了POI之间影响力不平衡问题
基于图卷积网络的推荐 HGCNR[22] GCN 对用户子图和兴趣点子图进行图卷积以获得更有效的节点信息 将复杂的多源异构数据分解为不同子图层以提取特征信息
TRGCN [23] GCN+attention mechanism 利用图卷积网络学习图中节点表示，提取不同时间下的用户偏好 考虑时间因素对用户偏好的影响
IGST−CL[32] GCN+attention mechanism+hypergraph 利用图卷积网络、时间注意力机制和超图网络等联合学习有效信息 能够缓解数据不平衡问题
HyperMST[33] GCN+attention mechanism+hypergraph 利用跨共享图卷积网络和多尺度超图注意机制建模地理和历史信息以及动态POI相关性 设置记忆增强嵌入和时空相干机制，更好整合时空关系
基于图自编码器的推荐 GraphCAR[35] GAE 融合多源信息，进行端到端训练，实现内容感知的多媒体推荐 具有一定的通用性和可扩展性
SIGA[38] GAE+Social Influence 将社交影响力与图自编码器相结合 更好地依据社交关系进行特征提取
HGATE[41] Stacked GAE+attention mechanism 堆叠自编码器与注意力机制融合提取节点信息及相关性 从用户自身偏好和依赖关系两方面结合进行推荐
MaskPOI[44] GAE+GAE 采用双层图自编码器共同捕获空间和属性信息 能够缓解数据集标签稀疏的问题
), ArticleFig(id=1242143427865948774, tenantId=1146029695717560320, journalId=1146031591421210625, articleId=1146098720635982590, language=CN, label=表1, caption=

图神经网络的各代表算法对比

, figureFileSmall=null, figureFileBig=null, tableContent=
类别 代表算法 主要技术 说明 优点
基于图注意力网络的推荐 IAGNN[15] GAT 利用分层注意力网络来捕捉用户对POI的全局和局部偏好 更好地捕捉用户意图，实现个性化推荐
MGAN[16] GAT 利用图注意力网络从多张特征图中学习POI表示 在统一模型中从多方面学习用户偏好
GE2AT[17] GAT+multi−layer perceptron 将注意力机制引入二分图中共同学习用户偏好特征 充分考虑用户的社会影响力，有效缓解数据稀疏性问题
SLS−REC[19] GAT+dynamic propagation mechanism 利用注意力机制捕获用户与POI的时间演化 融合时间因素，缓解了POI之间影响力不平衡问题
基于图卷积网络的推荐 HGCNR[22] GCN 对用户子图和兴趣点子图进行图卷积以获得更有效的节点信息 将复杂的多源异构数据分解为不同子图层以提取特征信息
TRGCN [23] GCN+attention mechanism 利用图卷积网络学习图中节点表示，提取不同时间下的用户偏好 考虑时间因素对用户偏好的影响
IGST−CL[32] GCN+attention mechanism+hypergraph 利用图卷积网络、时间注意力机制和超图网络等联合学习有效信息 能够缓解数据不平衡问题
HyperMST[33] GCN+attention mechanism+hypergraph 利用跨共享图卷积网络和多尺度超图注意机制建模地理和历史信息以及动态POI相关性 设置记忆增强嵌入和时空相干机制，更好整合时空关系
基于图自编码器的推荐 GraphCAR[35] GAE 融合多源信息，进行端到端训练，实现内容感知的多媒体推荐 具有一定的通用性和可扩展性
SIGA[38] GAE+Social Influence 将社交影响力与图自编码器相结合 更好地依据社交关系进行特征提取
HGATE[41] Stacked GAE+attention mechanism 堆叠自编码器与注意力机制融合提取节点信息及相关性 从用户自身偏好和依赖关系两方面结合进行推荐
MaskPOI[44] GAE+GAE 采用双层图自编码器共同捕获空间和属性信息 能够缓解数据集标签稀疏的问题
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方金凤 , 陈祖颐
科技导报 | 研究论文 2025,43(9): 76-83
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科技导报 |研究论文 2025 , 43 (9) : 76 -83
基于图神经网络的兴趣点推荐方法研究进展
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方金凤，讲师，研究方向为时空大数据、兴趣点推荐、轨迹预测等，电子信箱：

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方金凤，讲师，研究方向为时空大数据、兴趣点推荐、轨迹预测等，电子信箱：

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方金凤 , 陈祖颐
作者信息
  • 辽宁工程技术大学电子与信息工程学院, 葫芦岛 125105
Research progress on point-of-interest recommendation methods based on graph neural networks
Jinfeng FANG , Zuyi CHEN
Affiliations
  • School of Electronic and Information Engineering, Liaoning Technical University, Huludao 125105, China
出版时间: 2025-05-13 doi: 10.3981/j.issn.1000-7857.2024.01.00099
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摘要
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交叉学科是新科学的生长点，是科学发展的必然趋势。基于位置服务的重要应用——兴趣点推荐，作为计算机学科与地理信息学科相交叉的研究课题，对于推动2个学科在时空数据分析等相关领域的交叉融合研究具有重要作用。分析了兴趣点推荐的影响因素即地理位置、时间因素、社交关系和流行度，重点阐述了基于图神经网络的兴趣点推荐方法，包括基于图注意力网络、图卷积网络、图自编码器的推荐，并对其特点进行对比；讨论了在兴趣点推荐中存在的一些关键挑战，如数据稀疏性、冷启动问题和用户动态偏好问题，并针对各项挑战提出相应的解决思路，提出了结合多种影响因素的推荐，跨领域推荐以及动态偏好推荐的发展趋势。

兴趣点推荐  /  基于位置的服务  /  图神经网络  /  推荐算法

Cross-discipline is the growth point of new science and the inevitable trend of scientific development. The important application of location-based service, point−of−interest (POI) recommendation, as a research topic at the intersection of computer science and geographic information science, it plays an important role in promoting the cross−fertilization research of the two disciplines in the spatio−temporal data analysis and other related fields. This paper analyzes the influencing factors of POI recommendation, focuses on POI recommendation methods based on graph neural networks, including recommendation based on graph attention network, graph convolution network, and graph auto−encoder, with a comparative analysis of their respective characteristics; discusses some key challenges encountered in POI recommendation, such as data sparsity, cold start issues, and user dynamic preference issues, and proposes potential solution ideas to these challenges, makes prospects for the future development trend of POI recommendation.

POI recommendation  /  location-based service  /  graph neural network  /  recommendation algorithm
方金凤, 陈祖颐. 基于图神经网络的兴趣点推荐方法研究进展. 科技导报, 2025 , 43 (9) : 76 -83 . DOI: 10.3981/j.issn.1000-7857.2024.01.00099
Jinfeng FANG, Zuyi CHEN. Research progress on point-of-interest recommendation methods based on graph neural networks[J]. Science & Technology Review, 2025 , 43 (9) : 76 -83 . DOI: 10.3981/j.issn.1000-7857.2024.01.00099
正文
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随着移动网络的快速发展和智能设备的普遍应用，基于位置的服务（如Yelp、Foursquare、携程、美团、今日头条、Google地图等）逐渐深入到社会生活的各个领域，兴趣点推荐（point of interest recommendation，POI recommendation）作为基于位置服务的重要支撑技术已成为当前时空数据管理和人工智能领域的研究热点[1−2]。兴趣点推荐主要根据用户在位置社交网络（location−based social networks，LBSN）中的签到或打卡记录从大量地理位置中为用户推荐其可能感兴趣的地点。作为一种新型社交网络，位置社交网络不仅能够记录用户位置信息，还能提供用户与周围人和场所的互动信息。一方面，商家根据这些数据挖掘用户偏好、预测用户感兴趣的地点并向用户进行推荐，进而吸引大量的客户，提高商家的收益；另一方面，用户通过兴趣点推荐系统过滤无用信息，便于出行规划，解决了用户的选择恐惧症即“信息过载”问题。因此，基于位置社交网络的兴趣点推荐系统应运而生。
1 兴趣点推荐的影响因素
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兴趣点推荐是指对LBSN中的大量数据信息进行分析，进而为用户推荐其可能感兴趣的地点，其推荐效果受多种因素影响。（1）地理位置。移动用户位置不固定，考虑该因素能更准确获取用户兴趣偏好，用户通常倾向于访问距离较近的位置[3−6]。（2）时间因素。用户访问地点与时间紧密相关，如工作日和假期的访问地点差异明显，已有研究通过对时间属性建模、融入上下文信息等方式，将时间信息融入推荐模型以提升精确度[3−6]。（3）社交关系。人们的兴趣爱好选择常受社交圈影响，在推荐过程中融入社交关系，能缓解数据稀疏性问题并提高推荐精确性[5−6]，可通过计算用户偏好和相似性、寻找相似用户群体、整合好友间相似度和信任度等方法实现。（4）流行度。流行度高的地点会吸引众多用户[7]，可通过分析用户生成内容、提取流行度特征、挖掘流行时间等方式，将流行度融入推荐算法，为用户提供更精确的推荐。
2 基于图神经网络的推荐方法
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基于图神经网络的兴趣点推荐能够通过有效的图结构建模和复杂的特征学习[8−9]，综合考虑地理位置、时间、社交关系和流行度等多方面因素，例如，通过节点表示学习位置特征和时间动态性，通过社交网络信息建模用户间的关系，同时平衡推荐结果中的流行度偏差，从而提高推荐的个性化和准确性，更好地满足用户的多样化需求，如图 1所示。
2.1 基于图注意力网络的兴趣点推荐
基于图注意力网络（graph attention networks，GAT）的推荐[10−21]旨在对用户行为和兴趣点之间的关系图建模，从中学习用户偏好和行为规律，并利用图注意力机制捕获不同节点之间的关系权重，自动进行合理的权重分配，避免了计算复杂的矩阵操作。Li等[10]利用注意力机制学习图中节点信息和关注度，计算不同因素对每个推荐结果的影响，从而根据最具影响力的因素进行更精准地推荐。Zhang等[11]对复杂社会关系进行建模，利用注意力机制学习用户社交访问节点的权重，同时融入地理和时间特征预测用户偏好，提高推荐精度。Shi等[12]利用时间级注意力机制捕获不同时段对用户偏好的影响，结合个人偏好和时间特征为用户推荐兴趣点。Zhang等[13]利用图注意力网络分别捕捉用户对单个POI和区域POI的偏好，结合区域信息和用户偏好进行POI推荐。刘志中等[14]利用图注意力网络学习含有社交关系的用户特征，利用标号二部图神经网络学习含有用户POI交互偏好的用户特征与POI特征，利用会话图神经网络学习含有用户POI转移偏好的POI特征，最后融合上述特征对用户和POI进行表示。Wang等[15]提出一种意图感知图神经网络模型IAGNN，设计分层注意力网络来学习用户对POI的全局和局部偏好，同时利用GNN捕捉用户意图，从而实现更可靠的推荐。Wu等[16]提出多通道图注意力网络MGAN，利用图注意力机制从多张不同影响因素的特征图中学习POI表示，从多方面推测出用户偏好进行推荐。Gong等[17]提出双图注意力网络预测框架GE2AT，将注意力机制分别引入用户和位置、用户和社交的二分图中，根据周围不同权重的邻居节点共同学习用户的偏好特征。
鉴于用户兴趣的动态演进特性及多维度特征间的复杂交互关系，动态建模方法在兴趣点推荐中展现出显著优势，因此近期研究开始探索更精细化的动态建模方法与深度融合机制。Fan等[18]利用自注意力机制捕获用户签到和POI访问的时间特征，以更好地反映时间的动态变化，从而实现对用户动态兴趣的捕捉。Fu等[19]提出一种新型融合用户短期和长期兴趣的自监督POI推荐模型SLS−REC，该模型利用注意力机制捕获用户与兴趣点交互的时间演化，同时采用基于动态传播机制的图神经网络来突出关键特征信息，从而捕捉用户的兴趣进行推荐。Zhang等[20]基于相关属性构建异构图和同构图，结合两图中的用户信息和POI信息，并馈送到图注意力网络中，从而生成用户和POI的最终嵌入表示。Meng等[21]采用图注意力机制学习用户社交网络的表征，再根据用户兴趣点交互图学习POI的表征，最后推荐出最佳的兴趣点。
2.2 基于图卷积网络的兴趣点推荐
基于图卷积网络（graph convolutional network，GCN）的推荐[22−33]目前应用非常广泛，其核心思想是根据图和节点从局部图的邻域信息中迭代地聚合特征信息，并根据聚合得到的特征信息向用户推荐兴趣点。Li[22]提出一种异构图卷积网络算法HGCNR，对用户子图和兴趣点子图进行图卷积，捕获更丰富的用户信息和兴趣点信息，从而生成POI推荐结果。Wu[23]提出基于时域的图卷积网络TRGCN，通过GCN学习图中每个节点在特定时间的表示，同时预测用户对POI的偏好，推荐出更精确的兴趣点。Zhang等[24]利用多图卷积网络学习用户和POI的表示，并根据用户评论内容捕捉用户偏好，最终实现个性化推荐。Wu等[25]通过GCN分别从用户−POI图和时间−POI图中提取节点特征，并从时空上下文的角度预测用户对POI的偏好得分。Mo等[26]提出一种时间感知GCN模型来提取时间信息中包含的丰富协作信号，根据时间信息将用户签到划分为多个子图，并构建边传播模块以实现信息的传播。Liu等[27]采用改进的图卷积网络挖掘用户和POI在3类关系图中的协同影响，提取用户和POI的特征信息。Gan等[28]使用GCN提取用户与POI交互的连接信息及文本特征，并融合位置保护算法向用户推荐POI，同时缓解用户位置信息泄露问题。
GCN网络在处理图结构数据方面表现出色，能够有效利用图结构信息并对节点之间的关系进行建模。然而，仅通过GCN网络难以有效处理复杂的时空动态特性和多源异构信息。因此，近期的研究开始采用更复杂的建模技术，例如门控机制、超图网络和对比学习策略，这些方法能够更全面地捕捉用户行为的时空动态特性，增强推荐的准确性和鲁棒性。Anjiri等[29]构建了一个带有合并和细化门的门控GCN模型，用来获取用户和POI的信任网络图和空间邻接矩阵图中的同质现象，基于同质性效应实现兴趣点推荐。Luan等[30]提出使用异构图来建模具有空间和时间信息的用户签到轨迹，从异构图中提取子图，并为每个子图构建邻接矩阵，最后通过神经网络融合邻接矩阵以获得POI和POI类别的向量表示。Pan等[31]将图卷积神经网络和协同过滤算法相结合使用图像去噪编码器代替领域聚合，以更好捕捉用户与旅游兴趣点相邻节点之间的关系和特征。闵昭浩等[32]提出一种融合地理和时空信息的对比兴趣点推荐方法IGST−CL。采用图卷积网络捕获地理影响，采用时间注意力机制和超图网络联合学习用户−POI交互行为的动态表示，并基于对比学习策略进一步增强模型推荐性能。Zhao等[33]提出一种多尺度时空超相关网络HyperMST，该网络通过跨共享图卷积网络建模地理和历史信息，同时采用多尺度超图注意机制来捕获跨不同时间尺度的动态POI相关性。
2.3 基于图自编码器的兴趣点推荐
基于图自编码器（graph auto−encoder，GAE）的推荐[34−44]将聚合邻域节点的思想融入编码器中，使其能在图结构上进行传递信息和编码，从而半监督或无监督地学习图节点信息，并对图结构数据进行解码来充分提取用户节点与兴趣点间连接关系特征，是常规自编码器在图结构上的扩展。Xu等[35]提出基于图自编码器的内容感知推荐模型GraphCAR，将图卷积网络作为编码器分别为用户和项目建模，增强二者间的交互性以提供更精准的推荐。Chang等[36]利用图自编码器挖掘兴趣点访问和地理关系图，增强POI之间潜在的地理影响，根据POI特征估计用户偏好，推荐更准确的兴趣点。Yu等[37]利用2个编码器分别提取用户签到的兴趣点类别和时间特征，在时间模式中准确捕获用户偏好，提高兴趣点推荐的准确性。刘金鑫[38]提出一种融合社交影响的图自编码器推荐方法SIGA，将用户之间的社交影响力与GAE相结合，利用编码器获得用户的交互信息并提取特征，从而向用户推荐。
为进一步提升推荐系统的性能和适应性，研究人员开始在基于图自编码器的兴趣点推荐方法中引入创新的模型结构和学习策略。通过引入多视角学习、变分推理等技术，增强对用户行为复杂模式的捕捉能力，从而提供更加个性化和精准的推荐服务。Gan等[39]提出基于变分推理的图自编码器，将用户交互行为等信息编码为随机变量，并根据VIGA框架推测用户复杂兴趣，从而进行推荐。Abinaya等[40]将用户偏好作为自编码器的输入，并通过注意力机制将上下文特征与用户偏好相结合，以提供适合用户的兴趣点。Wang等 [41]提出一种异构图注意力自动编码器HGATE，通过堆叠的编码器解码器学习节点属性，同时融入分层注意力机制学习节点间的相关性，最终提取特征信息进行推荐。Zhu等[42]提出一种基于会话的新型模型，利用多视角变分图自编码器和多视角注意网络从共现属性图中学习统一的兴趣点表示。温雯等[43]利用变分自编码器和GNN捕获用户行为的交互关系，结合注意力机制推测用户访问POI的概率，以实现兴趣点推荐。Zhang等[44]结合了图神经网络和掩码建模的优势，提出自监督学习框架MaskPOI，联合基于边缘掩码的图自动编码器和基于特征掩码的图自动编码器共同捕获表示学习的空间和属性信息。表 1总结了GAT、GCN与GAE等方法中的代表算法、主要技术及优点。
3 面临的挑战和趋势展望
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3.1 面临的挑战
1）数据稀疏性。数据稀疏性是推荐过程中面临的关键问题，即在LBSN的海量兴趣点中用户只对小部分兴趣点进行了访问行为，无法获得充足的用户行为数据，造成数据高度稀疏[45−47]。此外，用户隐私保护机制的强化也导致部分敏感用户行为数据未被完整记录。这些因素均会导致兴趣点推荐数据集的高度稀疏性，使得难以准确学习到用户对未访问兴趣点的偏好程度，在很大程度上限制了兴趣点推荐结果的准确性。
2）冷启动问题。冷启动问题通常指新用户或新位置加入时推荐系统无法准确地预测用户兴趣偏好的问题[48−49]。新用户因缺乏历史交互记录导致个性化偏好难以建模（用户冷启动），新POI因缺失用户反馈而无法有效表征其特征属性（物品冷启动）。系统缺少足够的历史数据来了解用户与兴趣点之间的交互，从而无法建立可靠的模型。冷启动问题在兴趣点推荐中不可避免，是限制兴趣点推荐领域发展的重要挑战。
3）用户动态偏好问题。用户动态偏好问题指的是用户的偏好会随时间和环境市场而动态变化，导致推荐算法挖掘出的用户偏好信息可能不再准确匹配其当前的兴趣，该现象被称为“概念漂移”或“时间漂移”，表明用户的偏好已经从以前的状态演变到了新的状态[50−51]。目前许多兴趣点推荐方法忽略了不同时间偏好对用户当前决策行为的不同影响，难以获得较为理想的兴趣点推荐结果。因此，如何构建动态偏好学习模型，准确捕获用户不同时间段的偏好并为其做出精准推荐是目前该领域亟待解决的重点难题。
3.2 趋势展望
1）结合多种影响因素的推荐。目前的兴趣点推荐系统主要依据用户和位置2方面因素来进行推荐，如用户的个人偏好、访问兴趣点的时间、兴趣点的流行度等。随着对用户偏好了解的逐步深入，需要考虑更多的因素来提高推荐效果和用户满意度，如天气、交通等外界环境因素。此外，融合多种影响因素的推荐也能在一定程度上弥补稀疏数据，缓解数据稀疏性问题，提高推荐系统的覆盖率和准确性，为用户提供更好的个性化推荐体验，但多种影响因素的融合会增加计算复杂度，造成方法难以实现。因此，在推荐系统模型中高效地融入更多的影响因素变得极其重要，这也逐渐变成一个具有挑战的研究方向。
2）跨领域推荐。跨领域推荐是指在不同领域中为用户进行推荐的过程。通常情况下，一个推荐系统的建立与维护都会很消耗资源和时间，而不同领域之间的数据种类和特征差异较大，同时在不同领域推荐也为用户提供了更全面、多样、新颖的选择。并且跨领域推荐能够很好地解决冷启动问题，目前多数研究者通过挖掘不同领域内的相似用户或兴趣点从而进行推荐，例如，采用迁移学习技术[52]实现一种跨城市POI推荐，将用户偏好迁移到不同城市，在一定程度上有效缓解了冷启动问题。然而，现有的跨领域推荐多数会对用户数据迁移，如果可以在基于图神经网络的兴趣点推荐系统研究中实现从一个用户−兴趣点关系图过渡到其他关系图，就可以实现不同图神经网络模型之间的数据信息迁移，从而能够更全面地进行推荐。因此，基于图神经网络的跨领域推荐也是一个值得关注的研究方向。
3）动态偏好推荐。动态偏好问题是推荐系统中的一项重要挑战，推荐系统可以采用多种策略和技术，如聚类、跟踪兴趣漂移过程、分析用户历史行为和强化学习来适应用户兴趣的变化并实现更准确的个性化推荐。例如，通过混合神经网络可以实现用户复杂动态兴趣的建模，通过聚类分析用户多样性偏好，得出用户的偏好倾向。动态偏好推荐能够及时跟踪用户的兴趣变化，并根据最新数据进行实时推荐，避免了由于用户偏好发生动态变化而导致的推荐准确度降低现象，从而提供更符合用户当前需求的推荐内容。通过不断更新用户的偏好模型，系统可以适应用户的变化，提供更精准的推荐结果。因此，采用多种技术相结合来进行动态偏好推荐是一个值得尝试的研究方向。
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  • 辽宁省教育厅理工类项目(JYTQN2023211)
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doi: 10.3981/j.issn.1000-7857.2024.01.00099
  • 接收时间：2024-01-05
  • 首发时间：2025-06-29
  • 出版时间：2025-05-13
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  • 收稿日期：2024-01-05
  • 修回日期：2024-07-19
  • 录用日期：2025-04-15
基金
辽宁省教育厅理工类项目(JYTQN2023211)
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    辽宁工程技术大学电子与信息工程学院, 葫芦岛 125105
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