基于时空因果推断的下一个兴趣点推荐

doi:10.11947/j.AGCS.2025.20250202

测绘学报 ›› 2025, Vol. 54 ›› Issue (11): 2081-2096.doi: 10.11947/j.AGCS.2025.20250202

• 地图学与地理信息 • 上一篇

基于时空因果推断的下一个兴趣点推荐

叶欣宇¹^,²(), 徐胜华¹(), 刘纪平¹, 陈虹宇¹^,², 王琢璐³, 李维炼²^,⁴

^1.中国测绘科学研究院，北京　100036
^2.西南交通大学地球科学与工程学院，四川　成都　611756
^3.辽宁工程技术大学测绘与地理科学学院，辽宁　阜新　123000
^4.测绘科学与地球空间信息技术自然资源部重点试验室，北京　100036

收稿日期:2025-06-12 修回日期:2025-10-30 发布日期:2025-12-15
通讯作者: 徐胜华 E-mail:yxy01@my.swjtu.edu.cn;xushh@casm.ac.cn
作者简介:叶欣宇（2001—），男，硕士生，研究方向为兴趣点推荐。E-mail：yxy01@my.swjtu.edu.cn
基金资助:
国家自然科学基金(42371478);地理信息工程国家重点实验室、测绘科学与地球空间信息技术自然资源部重点实验室联合资助基金(2024-02-09)

Next point of interest recommendation based on spatio-temporal causal inference

Xinyu YE¹^,²(), Shenghua XU¹(), Jiping LIU¹, Hongyu CHEN¹^,², Zhuolu WANG³, Weilian LI²^,⁴

^1.Chinese Academy of Surveying and Mapping, Beijing 100036, China
^2.Faculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu 611756, China
^3.School of Geomatics, Liaoning Technical University, Fuxin 123000, China
^4.Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of MNR, Beijing 100036, China

Received:2025-06-12 Revised:2025-10-30 Published:2025-12-15
Contact: Shenghua XU E-mail:yxy01@my.swjtu.edu.cn;xushh@casm.ac.cn
About author:YE Xinyu (2001—), male, postgraduate, majors in POI recommendation.　E-mail: yxy01@my.swjtu.edu.cn
Supported by:
The National Natural Science Foundation of China(42371478);The Foundation of State Key Laboratory of Geo-Information Engineering and Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of MNR, CASM(2024-02-09)

摘要/Abstract

摘要：

下一个兴趣点推荐是基于位置的社交网络的一项重要服务，在用户个性化位置推荐、商业优化布局等领域具有显著应用价值。当前推荐系统主要基于深度学习方法而未采用因果推断框架，导致其倾向于学习均质化的相关性，难以有效消除时空数据中混杂因子的干扰，使得模型推荐性能受限。针对上述问题，本文提出基于时空因果推断的下一个兴趣点推荐模型。首先，为了将时空数据融入因果推断框架，分别基于时序特征、空间特征对序列信息和时空上下文信息嵌入编码，生成时空角度上的用户与兴趣点的特征表示。然后，基于前门调整和反事实推理提出因果推断模块，通过识别特征变量之间的因果效应，提取具有用户差异的用户时空状态，有效缓解了时空数据中混杂因子的影响。最后，处理用户多时段的时空状态，预测用户访问各个兴趣点的概率，实现下一个兴趣点推荐。在两个真实数据集上进行了试验分析，与最优的基线模型相比，本文方法的首项准确率和平均倒数排名在纽约数据集上分别提升了37.60%和22.72%，在东京数据集上分别提升了32.84%和20.63%，证明了本文方法的有效性和先进性。

关键词: 下一个兴趣点推荐, 时空上下文信息, 因果推断, 混杂因子

Abstract:

The next point of interest (POI) recommendation is a vital service within location-based social networks, demonstrating substantial application potential in domains such as personalized location recommendation for users and layout optimization for business. However, current recommendation systems predominantly leverage deep learning approaches without incorporating causal inference frameworks. This limitation results in a tendency to learn homogenized correlations, which renders the systems incapable of effectively mitigating the influence from confounding factors inherent in spatio-temporal data. Consequently, the recommendation performance of these models is significantly constrained. To address the mentioned issues, this paper proposes spatio-temporal causal inference network (STCIN), a next POI recommendation method based on spatio-temporal causal inference. First, to integrate spatio-temporal data into the causal inference framework, we design a spatio-temporal correlation embedding module. This module separately encodes sequential information and spatio-temporal contextual information through temporal feature-based and spatial feature-based embedding operations, generating user and POI feature representations from a spatio-temporal perspective. Next, we propose a causal-inference module based on front-door criterion and counterfactual reasoning. It discerns the causal effects among feature variables, and distills users' spatio-temporal states that capture individual heterogeneity, to alleviate the influence of confounding factors within spatio-temporal data. Finally, the model processes multi-period spatio-temporal states, estimating the probability that a user will visit each candidate POI, to achieve next POI recommendation. Extensive experiments conducted on two real-world datasets demonstrate that, compared to the best baseline model, the STCIN improves accuracy and mean reciprocal rank by 37.60% and 22.72% on New York dataset, and by 32.84% and 20.63% on Tokyo dataset. These results substantiate the effectiveness and superiority of the proposed STCIN model.

Key words: next POI recommendation, spatio-temporal context information, causal inference, confounding factor

中图分类号:

P208

叶欣宇, 徐胜华, 刘纪平, 陈虹宇, 王琢璐, 李维炼. 基于时空因果推断的下一个兴趣点推荐[J]. 测绘学报, 2025, 54(11): 2081-2096.

Xinyu YE, Shenghua XU, Jiping LIU, Hongyu CHEN, Zhuolu WANG, Weilian LI. Next point of interest recommendation based on spatio-temporal causal inference[J]. Acta Geodaetica et Cartographica Sinica, 2025, 54(11): 2081-2096.

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符号	描述
	用户签到记录
	时空动态特征
	用户偏好特征
	上下文特征
	用户时空状态
	预测目标

用户	POI	POI类别	POI类别名称	签到时间戳	经度	纬度
71	481dfa3af964a5207f4f1fe3	4bf58dd8d48988d1fa931735	Hotel（酒店）	2012-04-03	73.99°W	40.75°N
71	481dfa3af964a5207f4f1fe3	4bf58dd8d48988d1fa931735	Hotel（酒店）	20：08：03	73.99°W	40.75°N
65	4d66b1ef84f28cfa548a6e69	4bf58dd8d48988d153941735	Burrito Place（卷饼店）	2012-04-03	73.99°W	40.81°N
65	4d66b1ef84f28cfa548a6e69	4bf58dd8d48988d153941735	Burrito Place（卷饼店）	20：09：55	73.99°W	40.81°N
592	4b44a049f964a5201cf825e3	4bf58dd8d48988d1fd931735	Subway（地铁）	2012-04-03	74.01°W	40.71°N
592	4b44a049f964a5201cf825e3	4bf58dd8d48988d1fd931735	Subway（地铁）	20：11：13	74.01°W	40.71°N
267	4e2bada17d8b7deda6d3fda6	4f2a25ac4b909258e854f55f	Neighborhood（街区）	2012-04-03	73.83°W	40.87°N
267	4e2bada17d8b7deda6d3fda6	4f2a25ac4b909258e854f55f	Neighborhood（街区）	20：11：28	73.83°W	40.87°N
976	4b5ca9dcf964a520f83c29e3	4bf58dd8d48988d1fd931735	Subway（地铁）	2012-04-03	73.93°W	40.70°N
976	4b5ca9dcf964a520f83c29e3	4bf58dd8d48988d1fd931735	Subway（地铁）	20：11：45	73.93°W	40.70°N

数据集	用户数	POI类别数	POI数	签到数	轨迹数
NYC数据集	1075	318	5099	104 074	14 160
TKY数据集	2281	291	7844	361 430	44 692

模型	NYC数据集				TKY数据集
模型	Acc@1	Acc@5	Acc@10	MRR	Acc@1	Acc@5	Acc@10	MRR
FPMC	0.100 3	0.212 6	0.297 0	0.170 1	0.081 4	0.204 5	0.274 6	0.134 4
PRME	0.115 9	0.223 6	0.310 5	0.171 2	0.105 2	0.272 8	0.294 4	0.178 6
LSTM	0.130 5	0.271 9	0.328 3	0.185 7	0.133 5	0.272 8	0.327 7	0.183 4
ST-RNN	0.148 3	0.292 3	0.362 2	0.219 8	0.140 9	0.302 2	0.357 7	0.221 2
STGN	0.171 6	0.338 1	0.412 2	0.259 8	0.168 9	0.339 1	0.384 8	0.242 2
CFPRec	0.169 2	0.386 7	0.489 4	0.268 0	0.205 2	0.402 8	0.476 9	0.296 3
STAN	0.223 1	0.458 2	0.573 4	0.325 3	0.196 3	0.379 8	0.446 4	0.285 2
GETNext	0.243 5	0.508 9	0.614 3	0.362 1	0.225 4	0.441 7	0.528 7	0.326 2
MTNet	0.262 0	0.538 1	0.632 1	0.385 5	0.257 5	0.497 7	0.584 8	0.365 9
STCIN	0.360 5	0.603 3	0.701 0	0.473 1	0.342 1	0.568 3	0.658 0	0.441 4
提升/（%）	37.60	12.12	10.90	22.72	32.84	14.18	12.52	20.63

试验组名称	时空关联嵌入	循环神经网络	图神经网络	反事实推理增强
STCIN	√	√	√	√
STCIN-P	√	×	√	√
STCIN-GEO	√	√	×	√
STCIN-R	√	√	√	×
STCIN-C	√	×	×	×

基于时空因果推断的下一个兴趣点推荐

Next point of interest recommendation based on spatio-temporal causal inference

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