城市街区和签到数据结合的个性化城市兴趣区域推荐方法

doi:10.11947/j.AGCS.2022.20210094

测绘学报 ›› 2022, Vol. 51 ›› Issue (8): 1797-1806.doi: 10.11947/j.AGCS.2022.20210094

城市街区和签到数据结合的个性化城市兴趣区域推荐方法

刘纪平¹, 张志然^1,2, 杨超伟³, 徐胜华¹, 陈才⁴, 仇阿根¹, 张福浩¹

1. 中国测绘科学研究院, 北京 100830;
2. 西安石油大学地球科学与工程学院, 陕西西安 710065;
3. 乔治梅森大学时空创新中心, 弗吉尼亚费尔法克斯 22030;
4. 江苏海洋大学海洋技术与测绘学院, 江苏连云港 222005

收稿日期:2021-02-22 修回日期:2021-12-28 发布日期:2022-09-03
通讯作者: 张志然 E-mail:zrzhang@xsyu.edu.cn
作者简介:刘纪平(1967-),男,博士,研究员,研究方向为政府地理空间大数据、政府地理信息服务、应急地理信息服务等。E-mail:liujp@casm.ac.cn
基金资助:
地理信息工程国家重点实验室、自然资源部测绘科学与地球空间信息技术重点实验室联合资助基金项目(2021-04-13);国家自然科学基金(42071384);国家重点研发计划(2019YFB2102503)

Personalized city region of interests recommendation method based on city block and check-in data

LIU Jiping¹, ZHANG Zhiran^1,2, YANG Chaowei³, XU Shenghua¹, CHEN Cai⁴, QIU Agen¹, ZHANG Fuhao¹

1. Chinese Academy of Surveying and Mapping, Beijing 100830, China;
2. School of Earth Sciences and Engineering, Xi'an Shiyou University, Xi'an 710065, China;
3. NSF Spatiotemporal Innovation Center, George Mason University, Fairfax Virglnia 22030, USA;
4. School of Marine Technology and Geomatics, Jiangsu Ocean University, Lianyungang 222005, China

Received:2021-02-22 Revised:2021-12-28 Published:2022-09-03
Supported by:
State Key Laboratory of Geo-Information Engineering and Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of MNR,CASM (No.2021-04-13);The National Natural Science Foundation of China (No.42071384);The National Key Research and Development Program of China (No.2019YFB2102503)

摘要/Abstract

摘要： 利用社交媒体用户的历史签到数据分析用户空间活动偏好实现用户兴趣区域推荐,在城市商业规划中起着重要作用,也为了解人们的城市生活和需求提供帮助。已有方法获得的ROI具有模糊性和多样性,无法给ROI赋予准确的地理描述信息,对用户来说可解释性不强。因此,本文提出了一种结合城市街区和签到数据的个性化兴趣区域推荐方法(CBCD),引入城市街区概念解决ROI边界模糊问题。首先,通过城市道路网生成城市街区,并将大规模签到数据映射到城市街区转换为区域签到;然后,基于区域签到对用户空间活动偏好和类别偏好分别进行个性化建模;最后,融合空间和类别活动偏好,向用户推荐其可能感兴趣的区域。在真实的数据集上进行试验,结果表明该方法具有较高的推荐精度,对用户感兴趣城市街区的挖掘和推荐具有一定的价值。

关键词: 兴趣区域, 推荐, 城市街区, 基于位置的社交网络

Abstract: Using the historical user data of social media to analyze the preferences of users'spatial activities and region of interest (ROI) is of great value for city commercial planning,people's urban life and needs.The ROI obtained by existing methods has big ambiguity and vague in content,and lack actual geographical scope and accurate geographical description for users.We propose a personalized ROI mining and recommendation method combing city block and check-in data (CBCD) that introduces the concept of city blocks to solve the problem of vague ROI boundaries.Specifically,the large number of check-in points are mapped into city blocks generated by the road network,followed by modeling of user geographical and categorical preference.Finally,we integrate geographical and categorical activity preference models to recommend ROIs to users.Experiments on real datasets show that this method has high recommendation accuracy and is of great value for mining and recommending city blocks of interest to users.

Key words: region of interest, recommendation, city block, location based social networks

中图分类号:

P227

刘纪平, 张志然, 杨超伟, 徐胜华, 陈才, 仇阿根, 张福浩. 城市街区和签到数据结合的个性化城市兴趣区域推荐方法[J]. 测绘学报, 2022, 51(8): 1797-1806.

LIU Jiping, ZHANG Zhiran, YANG Chaowei, XU Shenghua, CHEN Cai, QIU Agen, ZHANG Fuhao. Personalized city region of interests recommendation method based on city block and check-in data[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(8): 1797-1806.

参考文献

[1] 冯浩,黄坤,李晶,等.基于深度学习的混合兴趣点推荐算法[J].电子与信息学报, 2019, 41(4):880-887. FENG Hao, HUANG Kun, LI Jing, et al. Hybrid point of interest recommendation algorithm based on deep learning[J]. Journal of Electronics and Information Technology, 2019, 41(4):880-887.
[2] LIU Yong, WEI Wei, SUN Aixin, et al. Exploiting geograp-hical neighborhood characteristics for location recommendation[C]//Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management. Shanghai, China:ACM Press,2014:739-748.
[3] YUAN Quan, CONG Gao, MA Zongyang, et al. Time-aware point-of-interest recommendation[C]//Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval. Dublin, Ireland:SIGIR,2013:363-372.
[4] REN Yueqiang, WANG Ze, SUN Xiaona, et al. A multi-element hybrid location recommendation algorithm for location based social networks[J].IEEE Access, 2019, 7:100416-100427.
[5] ZHAO Kangzhi, ZHANG Yong, YIN Hongzhi, et al. Discovering subsequence patterns for next POI recommendation[C]//Proceedings of the 29th International Joint Conference on Artificial Intelligence. Yokohama, Japan:AIO,2020:3216-3222.
[6] YE Mao, YIN Peifeng, LEE W, et al. Exploiting geographical influence for collaborative point-of-interest recommendation[C]//Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval. Beijing, China:ACM Press,2011:325-334.
[7] LI Huayu, GE Yong, HONG Richang, et al. Point-of-interest recommendations:learning potential check-ins from friends[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Francisco, California, USA:ACM Press,2016:975-984.
[8] 任星怡,宋美娜,宋俊德.基于位置社交网络的上下文感知的兴趣点推荐[J].计算机学报, 2017, 40(4):824-841. REN Xingyi, SONG Meina, SONG Junde. Context-aware point-of-interest recommendation in location-based social networks[J]. Chinese Journal of Computers, 2017, 40(4):824-841.
[9] SUN Ying, ZHU Hengshu, ZHUANG Fuzhen, et al. Exploring the urban region-of-interest through the analysis of online map search queries[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. London, United Kingdom:ACM Press, 2018:2269-2278.
[10] 景宁,王跃华,钟志农,等.地理社交网络位置推荐[J].国防科技大学学报, 2015, 37(5):1-8. JING Ning, WANG Yuehua, ZHONG Zhinong, et al. Location recommendation on location-based social networks[J]. Journal of National University of Defense Technology, 2015, 37(5):1-8.
[11] ZHANG Jiadong, CHOW C, LI Yanhua. iGeoRec:a personalized and efficient geographical location recommendation framework[J]. IEEE Transactions on Services Computing, 2015, 8(5):701-714.
[12] KONG Dejiang, WU Fei. HST-LSTM:a hierarchical spatial-temporal long-short term memory network for location prediction[C]//Proceedings of the 27th International Joint Conferences on Artificial Intelligence Organization. Stockholm, Sweden:AIO, 2018:2341-2347.
[13] XU Hengpeng, WEI Jinmao, YANG Zhenglu, et al. Graph attentive network for region recommendation with POI-and ROI-level attention[M]//Web and Big Data. Cham:Springer International Publishing, 2020:509-516.
[14] LIU Zhewei, ZHOU Xiaolin, SHI Wenzhong, et al. Recommending attractive thematic regions by semantic community detection with multi-sourced VGI data[J]. International Journal of Geographical Information Science, 2019, 33(8):1520-1544.
[15] PHAM T N, LI Xutao, CONG Gao. A general model for out-of-town region recommendation[C]//Proceedings of the 26th International Conference on World Wide Web. Perth, Australia:IW3C2, 2017:401-410.
[16] YANG Dingqi, ZHANG Daqing, ZHENG V W, et al. Modeling user activity preference by leveraging user spatial temporal characteristics in LBSNs[J]. IEEE Transactions on Systems, Man, and Cybernetics:Systems, 2015, 45(1):129-142.
[17] YUAN Quan, ZHANG Wei, ZHANG Chao, et al. PRED:periodic region detection for mobility modeling of social media users[C]//Proceedings of the 10th ACM International Conference on Web Search and Data Mining. Cambridge, United Kingdom:WSDM, 2017:263-272.
[18] BAO Yi, HUANG Zhou, LI Linna, et al. A BiLSTM-CNN model for predicting users'next locations based on geotagged social media[J]. International Journal of Geographical Information Science, 2020, 35(4):639-660.
[19] TAN Rong, ZHANG Yunpeng. Road network-based region of interest mining and social relationship recommendation[J]. Soft Computing, 2019, 23(19):9299-9313.
[20] GRAAFF V D, BY R D, KEULEN M V, et al. Point of interest to region of interest conversion[C]//Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. Orlando, Florida, USA:ACM Press, 2013:388-391.
[21] 刘纪平,张用川,徐胜华,等.一种顾及道路复杂度的增量路网构建方法[J].测绘学报, 2019, 48(4):480-488.DOI:10.11947/j.AGCS.2019.20180419. LIU Jiping, ZHANG Yongchuan, XU Shenghua, et al. An incremental construction method of road network considering road complexity[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(4):480-488.DOI:10.11947/j.AGCS.2019.20180419.
[22] 陈占龙,周路林,禹文豪,等.顾及兴趣点潜在上下文关系的城市功能区识别[J].测绘学报, 2020, 49(7):907-920.DOI:10.11947/j.AGCS.2020.20190315. CHEN Zhanlong, ZHOU Lulin, YU Wenhao, et al. Identification of the urban functional regions considering the potential context of interest points[J]. Acta Geodaetica et Cartographica Sinica, 2020, 49(7):907-920.DOI:10.11947/j.AGCS.2020.20190315.
[23] CURL J S, ABBEVILLE A, ALTAMIRA A, et al. A dictionary of architecture and landscape architecture[M]. 2ed.Oxford:Oxford University Press, 2006.
[24] YIN Hongzhi, SUN Yizhou, CUI Bin, et al. LCARS:a location-content-aware recommender system[C]//Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Chicago, Illinois, USA:ACM Press, 2013:221-229.
[25] LIU Jiping, ZHANG Zhiran, LIU Chunyang, et al. Exploiting two-dimensional geographical and synthetic social influences for location recommendation[J]. ISPRS International Journal of Geo-Information, 2020, 9(4):285.
[26] 张国明,王俊淑,江南,等.关注点推荐算法的霍克斯过程法[J].测绘学报, 2018, 47(9):1261-1269.DOI:10.11947/j.AGCS.2018.20170552. ZHANG Guoming, WANG Junshu, JIANG Nan, et al. A point-of-interest recommendation method based on hawkes process[J]. Acta Geodaetica et Cartographica Sinica, 2018, 47(9):1261-1269.DOI:10.11947/j.AGCS.2018.20170552.
[27] CHENG Chen, YANG Haiqin, KING I, et al. Fused matrix factorization with geographical and social influence in location-based social networks[C]//Proceedings of the 26th AAAI Conference on Artificial Intelligence. Toronto, Ontario, Canada:AAAI, 2012:17-23.

城市街区和签到数据结合的个性化城市兴趣区域推荐方法

Personalized city region of interests recommendation method based on city block and check-in data

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