计算机科学与探索 ›› 2022, Vol. 16 ›› Issue (7): 1462-1478.DOI: 10.3778/j.issn.1673-9418.2112037
收稿日期:
2021-12-09
修回日期:
2022-02-14
出版日期:
2022-07-01
发布日期:
2022-07-25
作者简介:
陈江美(1995—),女,福建南平人,博士研究生,主要研究方向为商务智能、数据挖掘等。 基金资助:
CHEN Jiangmei1, ZHANG Wende2,+()
Received:
2021-12-09
Revised:
2022-02-14
Online:
2022-07-01
Published:
2022-07-25
Supported by:
摘要:
兴趣点推荐是近年来位置社交网络和推荐系统领域研究的热点之一,了解兴趣点推荐在位置社交网络方面的发展现状,有利于为下一步的研究提供方向。对国内外兴趣点推荐系统的相关文献进行梳理,首先介绍了兴趣点推荐系统的概念,并从影响推荐的因素、推荐方法和推荐存在的问题三方面探讨其与传统推荐的区别。然后提出了兴趣点推荐系统的基本框架,该框架包含了数据来源、推荐方法和算法评价三个核心部分。以该框架为基础,介绍了影响兴趣点推荐的多种因素,归纳了现有的兴趣点推荐算法,总结了算法的评价指标。同时对代表性工作进行了分析介绍,详细总结了各种方法的研究内容与特点,并评价了其优势与不足。最后对该领域所面临的挑战和潜在的研究方向进行了总结与展望,给出了未来的研究趋势和发展方向。
中图分类号:
陈江美, 张文德. 基于位置社交网络的兴趣点推荐系统研究综述[J]. 计算机科学与探索, 2022, 16(7): 1462-1478.
CHEN Jiangmei, ZHANG Wende. Review of Point of Interest Recommendation Systems in Location-Based Social Networks[J]. Journal of Frontiers of Computer Science and Technology, 2022, 16(7): 1462-1478.
比较类型 | 兴趣点推荐系统 | 传统的推荐系统 |
---|---|---|
位置维度 | 有 | 无 |
信息来源 | 签到数据、情景信息 | 评分信息 |
数据类型 | 隐式数据 | 显式数据 |
推荐结果展示 | 基于评价的方式、列表方式 | 基于评价的方式 |
解决问题 | 数据稀疏、冷启动、序列推荐、动态推荐、个性化推荐、异地推荐 | 数据稀疏、冷启动 |
表1 传统推荐与兴趣点推荐的区别
Table 1 Difference between traditional recommendation and POI recommendation
比较类型 | 兴趣点推荐系统 | 传统的推荐系统 |
---|---|---|
位置维度 | 有 | 无 |
信息来源 | 签到数据、情景信息 | 评分信息 |
数据类型 | 隐式数据 | 显式数据 |
推荐结果展示 | 基于评价的方式、列表方式 | 基于评价的方式 |
解决问题 | 数据稀疏、冷启动、序列推荐、动态推荐、个性化推荐、异地推荐 | 数据稀疏、冷启动 |
代表算法 | 影响推荐因素 | 说明 | 优点 | 局限性 | 代码链接 |
---|---|---|---|---|---|
USG[ | 用户偏好、地理、社交 | 将用户偏好融合到具有社会和地理影响的推荐框架 | 整合了多种情景信息 | 无法解决数据稀疏问题 | http://spatialkeyword.sce.ntu.edu.sg/eval-vldb17/code/ USG.zip |
GeoMF[ | 地理 | 融合地理信息到加权矩阵分解算法模型 | 捕捉了用户的空间聚类现象 | 只考虑了地理位置影响 | http://spatialkeyword.sce.ntu.edu.sg/eval-vldb17/code/GeoMF.zip |
iGSLR[ | 地理、社交 | 将社会影响和个性化地理影响整合到统一的推荐框架 | 实现了个性化的推荐 | 无法解决冷启动问题和数据稀疏问题 | https://github.com/camcochet/iGSLR-Personalized-Geo-Social-Location-Recommendation |
LRT[ | 时间 | 提出时间的连续性和差异性特征,融入矩阵分解框架中 | 模拟了时间特征影响;实现了动态推荐 | 推荐准确率较低;只考虑了时间影响 | http://spatialkeyword.sce.ntu.edu.sg/eval-vldb17/code/LRT.zip |
GTSCP[ | 地理、社交、时间、内容、流行度 | 将所有情景信息融入联合概率生成模型 | 适用于异地推荐场景;缓解数据稀疏问题 | 未解决兴趣点特征缺失的不足 | N/A |
LGLMF[ | 地理、流行度 | 考虑用户的主要活动区域及该区域内每个位置的相关性 | 可缓解数据稀疏性问题 | 未充分考虑其他情景信息的影响 | https://paperswithcode.com/paper/lglmf-local-geographical-based-logistic#code |
SLGMF[ | 地理、社交 | 融合社交和局部地理因素影响 | 可缓解数据稀疏性问题 | 融合的情景信息较有限 | N/A |
CRQA[ | 地理、内容 | 将空间和地理信息融合到联合推理模型中 | 可扩展到其他领域的推荐任务 | 只考虑了空间和文本推理 | https://paperswithcode.com/paper/joint-spatio-textual-reasoning-for-answering#code |
MM-Gated-XAtt[ | 内容、图像 | 从各模态中提取相关信息来获取文本和图像间的交互 | 有效捕获多模态间的交互 | 未充分利用兴趣点与用户信息 | https://github.com/danaesavi/poi-type-prediction |
表2 兴趣点推荐的影响因素中各代表算法对比
Table 2 Comparison of typical algorithms in influencing factors of POI recommendation
代表算法 | 影响推荐因素 | 说明 | 优点 | 局限性 | 代码链接 |
---|---|---|---|---|---|
USG[ | 用户偏好、地理、社交 | 将用户偏好融合到具有社会和地理影响的推荐框架 | 整合了多种情景信息 | 无法解决数据稀疏问题 | http://spatialkeyword.sce.ntu.edu.sg/eval-vldb17/code/ USG.zip |
GeoMF[ | 地理 | 融合地理信息到加权矩阵分解算法模型 | 捕捉了用户的空间聚类现象 | 只考虑了地理位置影响 | http://spatialkeyword.sce.ntu.edu.sg/eval-vldb17/code/GeoMF.zip |
iGSLR[ | 地理、社交 | 将社会影响和个性化地理影响整合到统一的推荐框架 | 实现了个性化的推荐 | 无法解决冷启动问题和数据稀疏问题 | https://github.com/camcochet/iGSLR-Personalized-Geo-Social-Location-Recommendation |
LRT[ | 时间 | 提出时间的连续性和差异性特征,融入矩阵分解框架中 | 模拟了时间特征影响;实现了动态推荐 | 推荐准确率较低;只考虑了时间影响 | http://spatialkeyword.sce.ntu.edu.sg/eval-vldb17/code/LRT.zip |
GTSCP[ | 地理、社交、时间、内容、流行度 | 将所有情景信息融入联合概率生成模型 | 适用于异地推荐场景;缓解数据稀疏问题 | 未解决兴趣点特征缺失的不足 | N/A |
LGLMF[ | 地理、流行度 | 考虑用户的主要活动区域及该区域内每个位置的相关性 | 可缓解数据稀疏性问题 | 未充分考虑其他情景信息的影响 | https://paperswithcode.com/paper/lglmf-local-geographical-based-logistic#code |
SLGMF[ | 地理、社交 | 融合社交和局部地理因素影响 | 可缓解数据稀疏性问题 | 融合的情景信息较有限 | N/A |
CRQA[ | 地理、内容 | 将空间和地理信息融合到联合推理模型中 | 可扩展到其他领域的推荐任务 | 只考虑了空间和文本推理 | https://paperswithcode.com/paper/joint-spatio-textual-reasoning-for-answering#code |
MM-Gated-XAtt[ | 内容、图像 | 从各模态中提取相关信息来获取文本和图像间的交互 | 有效捕获多模态间的交互 | 未充分利用兴趣点与用户信息 | https://github.com/danaesavi/poi-type-prediction |
推荐算法类别 | 代表算法 | 说明 | 优点 | 局限性 | 代码链接 |
---|---|---|---|---|---|
基于矩阵分解的兴趣点推荐 | ASMF[ | 考虑三类朋友来寻找每个用户的潜在兴趣点,并将地理和分类信息融入矩阵分解框架中 | 考虑了朋友的影响,同时可缓解数据稀疏问题 | 融合的情景信息较有限 | https://paperswithcode.com/paper/point-of-interest-recommendations-learning#code |
GeoMF[ | 利用加权矩阵分解算法融合地理信息 | 捕捉了用户的空间聚类现象 | 考虑的情景信息单一 | http://spatialkeyword.sce.ntu.edu.sg/eval-vldb17/code/GeoMF.zip | |
STACP[ | 将时空信息合并到矩阵分解模型中 | 可缓解数据稀疏问题;实现动态推荐 | 无法解决冷启动问题 | https://paperswithcode.com/paper/joint-geographical-and-temporal-modeling#code | |
基于图嵌入的兴趣点推荐 | UP2VEC[ | 利用异构图融合用户的社交、地理和时间信息 | 解决社交网络的异构性问题 | 模型的复杂度较高 | N/A |
RELINE[ | 利用基于图的方法从各信息关系图中学习用户和兴趣点表示,并嵌入到潜在空间中 | 解决冷启动问题 | 未考虑节点属性 | https://paperswithcode.com/paper/reline-point-of-interest-recommendations#code | |
GLR[ | 利用基于图嵌入的方法捕获时间序列和用户时间偏好信息 | 可有效模拟用户复杂的移动行为 | 可解释性较低 | N/A | |
基于深度学习的兴趣点推荐 | DAN-SNR[ | 利用自注意力机制建模序列信息和社交影响 | 可有效表征用户与兴趣点的特征 | 模型的复杂度较高 | https://paperswithcode.com/paper/dan-snr-a-deep-attentive-network-for-social#code |
GNN-POI[ | 利用图神经网络构建用户-兴趣点节点的网络图 | 适用于各类推荐任务 | 模型的复杂度较高 | N/A | |
STAN[ | 一种时空双向注意模型,充分考虑了相关位置的时空效应 | 考虑了非相邻位置和非连续签到位置的相关性 | 融合的情景信息较有限 | https://paperswithcode.com/paper/stan-spatio-temporal-attention-network-for-1#code |
表3 兴趣点推荐方法中各代表算法对比
Table 3 Comparison of typical algorithms in POI recommendation methods
推荐算法类别 | 代表算法 | 说明 | 优点 | 局限性 | 代码链接 |
---|---|---|---|---|---|
基于矩阵分解的兴趣点推荐 | ASMF[ | 考虑三类朋友来寻找每个用户的潜在兴趣点,并将地理和分类信息融入矩阵分解框架中 | 考虑了朋友的影响,同时可缓解数据稀疏问题 | 融合的情景信息较有限 | https://paperswithcode.com/paper/point-of-interest-recommendations-learning#code |
GeoMF[ | 利用加权矩阵分解算法融合地理信息 | 捕捉了用户的空间聚类现象 | 考虑的情景信息单一 | http://spatialkeyword.sce.ntu.edu.sg/eval-vldb17/code/GeoMF.zip | |
STACP[ | 将时空信息合并到矩阵分解模型中 | 可缓解数据稀疏问题;实现动态推荐 | 无法解决冷启动问题 | https://paperswithcode.com/paper/joint-geographical-and-temporal-modeling#code | |
基于图嵌入的兴趣点推荐 | UP2VEC[ | 利用异构图融合用户的社交、地理和时间信息 | 解决社交网络的异构性问题 | 模型的复杂度较高 | N/A |
RELINE[ | 利用基于图的方法从各信息关系图中学习用户和兴趣点表示,并嵌入到潜在空间中 | 解决冷启动问题 | 未考虑节点属性 | https://paperswithcode.com/paper/reline-point-of-interest-recommendations#code | |
GLR[ | 利用基于图嵌入的方法捕获时间序列和用户时间偏好信息 | 可有效模拟用户复杂的移动行为 | 可解释性较低 | N/A | |
基于深度学习的兴趣点推荐 | DAN-SNR[ | 利用自注意力机制建模序列信息和社交影响 | 可有效表征用户与兴趣点的特征 | 模型的复杂度较高 | https://paperswithcode.com/paper/dan-snr-a-deep-attentive-network-for-social#code |
GNN-POI[ | 利用图神经网络构建用户-兴趣点节点的网络图 | 适用于各类推荐任务 | 模型的复杂度较高 | N/A | |
STAN[ | 一种时空双向注意模型,充分考虑了相关位置的时空效应 | 考虑了非相邻位置和非连续签到位置的相关性 | 融合的情景信息较有限 | https://paperswithcode.com/paper/stan-spatio-temporal-attention-network-for-1#code |
影响因素和问题 | 推荐策略 | 推荐算法 | |
---|---|---|---|
影响因素 | 地理信息 | 考虑用户签到兴趣点的概率与物理距离相关,提出幂律分布模型(Ye等[ | Hybrid |
考虑用户在多个中心点范围内签到,提出多中心的高斯分布模型(Cheng等[ | MF | ||
提出核密度估计方法建模用户的地理偏好(Zhang等[ | Hybrid | ||
提出固定带宽核密度估计方法构建地理偏好模型(Zhang等[ | — | ||
利用自适应核密度估计方法捕捉用户的地理偏好(任星怡等[ | MF等 | ||
用户偏好 | 将情景信息融入矩阵分解框架(任星怡等[ | MF | |
采用基于用户的协同过滤方法建模签到信息和地理信息(Song等[ | CF | ||
将好友重要性与签到相关性融入协同过滤方法(Zhou等[ | CF | ||
利用卷积神经网络挖掘用户评论中的语义信息(Xing等[ | CNN | ||
社交 | 利用概率矩阵分解模型建模社交关系(Qian等[ | MF | |
提出基于朋友的协同过滤方法(彭宏伟等[ | CF | ||
将改进后的直接信任与间接信任融入矩阵分解算法(Xu等[ | MF | ||
利用图嵌入的方法表示用户间的社交关系(Zhu等[ | GE | ||
利用图神经网络构建社交网络图(Zhang等[ | GNN | ||
时间 | 提出基于时间感知的兴趣点推荐模型(Yuan等[ | Hybrid | |
提出时间的差异性和连续性特征(Gao等[ | MF | ||
提出基于非对称投影的时间感知嵌入方法(Ying等[ | — | ||
利用时间信息捕捉用户的动态偏好(Ma等[ | RNN、Hybrid | ||
利用时间序列数据实现下一个兴趣点推荐(Chen等[ | RNN、Hybrid | ||
流行度 | 考虑兴趣点流行度影响用户的决策行为(Yuan等[ | Hybrid、MF | |
利用文本和图像信息预测兴趣点流行度(Yang等[ | — | ||
将流行度特征结合二维的核密度估计与一维的幂律分布(Si等[ | — | ||
内容 | 将兴趣点的文本内容嵌入深度学习模型(Chen等[ | Hybrid | |
采用加权矩阵分解算法融合图像内容和地理信息(Zhang等[ | MF | ||
推荐问题 | 数据稀疏性 | 利用矩阵分解技术缓解稀疏问题(Gao等[ | MF |
利用图嵌入表示方法缓解稀疏性(Zhu等[ | GE | ||
采用混合图卷积网络应对稀疏性问题(Zhong等[ | GNN | ||
冷启动 | 利用元路径挖掘用户行为间复杂的语义关系以表征新用户的属性(Yu等[ | MF | |
提出混合图神经网络模型改善冷启动问题(Zhong等[ | GNN | ||
序列推荐 | 提出一种基于注意力机制的循环神经网络实现序列推荐(Xia等[ | RNN | |
提出改进的基于图的潜在表示模型来捕获时间序列影响(Lu等[ | Hybrid | ||
利用循环神经网络学习情景信息来预测用户下一个签到的兴趣点(Wang等[ | RNN | ||
动态推荐 | 提出兴趣点的受欢迎程度受时间影响(Yuan等[ | Hybrid | |
考虑用户偏好的动态变化特征(Gao等[ | MF、RNN等 | ||
利用协同过滤算法与模糊聚类算法捕捉用户的时空偏好(Yin等[ | Hybrid | ||
个性化推荐 | 提出核密度估计方法描述用户的地理行为(Zhang等[ | MF等 | |
融合流行度与二维的核密度估计方法捕捉活跃用户的偏好(Si等[ | — | ||
异地推荐 | 提出概率生成模型模拟用户在异地的决策行为(任星怡等[ | — |
表4 兴趣点推荐的研究现状分析
Table 4 Analysis of research status in POI recommendation
影响因素和问题 | 推荐策略 | 推荐算法 | |
---|---|---|---|
影响因素 | 地理信息 | 考虑用户签到兴趣点的概率与物理距离相关,提出幂律分布模型(Ye等[ | Hybrid |
考虑用户在多个中心点范围内签到,提出多中心的高斯分布模型(Cheng等[ | MF | ||
提出核密度估计方法建模用户的地理偏好(Zhang等[ | Hybrid | ||
提出固定带宽核密度估计方法构建地理偏好模型(Zhang等[ | — | ||
利用自适应核密度估计方法捕捉用户的地理偏好(任星怡等[ | MF等 | ||
用户偏好 | 将情景信息融入矩阵分解框架(任星怡等[ | MF | |
采用基于用户的协同过滤方法建模签到信息和地理信息(Song等[ | CF | ||
将好友重要性与签到相关性融入协同过滤方法(Zhou等[ | CF | ||
利用卷积神经网络挖掘用户评论中的语义信息(Xing等[ | CNN | ||
社交 | 利用概率矩阵分解模型建模社交关系(Qian等[ | MF | |
提出基于朋友的协同过滤方法(彭宏伟等[ | CF | ||
将改进后的直接信任与间接信任融入矩阵分解算法(Xu等[ | MF | ||
利用图嵌入的方法表示用户间的社交关系(Zhu等[ | GE | ||
利用图神经网络构建社交网络图(Zhang等[ | GNN | ||
时间 | 提出基于时间感知的兴趣点推荐模型(Yuan等[ | Hybrid | |
提出时间的差异性和连续性特征(Gao等[ | MF | ||
提出基于非对称投影的时间感知嵌入方法(Ying等[ | — | ||
利用时间信息捕捉用户的动态偏好(Ma等[ | RNN、Hybrid | ||
利用时间序列数据实现下一个兴趣点推荐(Chen等[ | RNN、Hybrid | ||
流行度 | 考虑兴趣点流行度影响用户的决策行为(Yuan等[ | Hybrid、MF | |
利用文本和图像信息预测兴趣点流行度(Yang等[ | — | ||
将流行度特征结合二维的核密度估计与一维的幂律分布(Si等[ | — | ||
内容 | 将兴趣点的文本内容嵌入深度学习模型(Chen等[ | Hybrid | |
采用加权矩阵分解算法融合图像内容和地理信息(Zhang等[ | MF | ||
推荐问题 | 数据稀疏性 | 利用矩阵分解技术缓解稀疏问题(Gao等[ | MF |
利用图嵌入表示方法缓解稀疏性(Zhu等[ | GE | ||
采用混合图卷积网络应对稀疏性问题(Zhong等[ | GNN | ||
冷启动 | 利用元路径挖掘用户行为间复杂的语义关系以表征新用户的属性(Yu等[ | MF | |
提出混合图神经网络模型改善冷启动问题(Zhong等[ | GNN | ||
序列推荐 | 提出一种基于注意力机制的循环神经网络实现序列推荐(Xia等[ | RNN | |
提出改进的基于图的潜在表示模型来捕获时间序列影响(Lu等[ | Hybrid | ||
利用循环神经网络学习情景信息来预测用户下一个签到的兴趣点(Wang等[ | RNN | ||
动态推荐 | 提出兴趣点的受欢迎程度受时间影响(Yuan等[ | Hybrid | |
考虑用户偏好的动态变化特征(Gao等[ | MF、RNN等 | ||
利用协同过滤算法与模糊聚类算法捕捉用户的时空偏好(Yin等[ | Hybrid | ||
个性化推荐 | 提出核密度估计方法描述用户的地理行为(Zhang等[ | MF等 | |
融合流行度与二维的核密度估计方法捕捉活跃用户的偏好(Si等[ | — | ||
异地推荐 | 提出概率生成模型模拟用户在异地的决策行为(任星怡等[ | — |
代表算法 | precision | recall | F1 | MAE | MAP | NDCG | 时间复杂度 | 空间复杂度 |
---|---|---|---|---|---|---|---|---|
LRT[ | √ | √ | | N/A | ||||
ASMF[ | √ | √ | √ | | N/A | |||
UCGSMF[ | √ | √ | | N/A | ||||
GSBPR[ | √ | √ | √ | √ | | N/A | ||
APRA-SA[ | √ | √ | √ | √ | | N/A | ||
SPR[ | √ | | | |||||
MANC[ | √ | √ | √ | √ | N/A | N/A | ||
ATST-LSTM[ | √ | √ | √ | | N/A | |||
Loc-Interest-LSTM[ | √ | √ | √ | | |
表5 几种代表算法的评价指标总结
Table 5 Summary of evaluation metrics of several algorithms
代表算法 | precision | recall | F1 | MAE | MAP | NDCG | 时间复杂度 | 空间复杂度 |
---|---|---|---|---|---|---|---|---|
LRT[ | √ | √ | | N/A | ||||
ASMF[ | √ | √ | √ | | N/A | |||
UCGSMF[ | √ | √ | | N/A | ||||
GSBPR[ | √ | √ | √ | √ | | N/A | ||
APRA-SA[ | √ | √ | √ | √ | | N/A | ||
SPR[ | √ | | | |||||
MANC[ | √ | √ | √ | √ | N/A | N/A | ||
ATST-LSTM[ | √ | √ | √ | | N/A | |||
Loc-Interest-LSTM[ | √ | √ | √ | | |
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