An intelligent recommendation method of multi-source remote sensing information considering user portrait

doi:10.11947/j.AGCS.2023.20210335

Abstract

Abstract: with the development of military-civilian-commercial satellites in recent years, many practical problems such as the lack of initiative and the lack of personalization in remote sensing data services have becoming increasingly serious, which restrict the intelligent application of remote sensing in multiple fields. Aiming at the problems, this paper designs an active recommendation method of remote sensing information based on user portrait model. Firstly, an extensible user portrait model was constructed that included five theme elements, including time, space, sensor, resolution and product level; Secondly, the weight, interval length and distribution characteristics of each element in the model are expressed and calculated in detail; Finally, combined with the weight and the interest characteristic of each element, the recommendation degree and the correlation degree between the data to be distributed and the interest characteristic value are calculated, so as to realize the ordered active recommendation of remote sensing data. Taking two users of the National Disaster Reduction Center of China and Beijing Anti-drug Brigade as examples, the experimental results based on real demand orders in past three years show that the distribution characteristics of the theme elements can objectively reflect the actual needs of users, and the average recommendation accuracy is better than 94%. The research results provide a model method for engineering realization of remote sensing data personalized service and intelligent recommendation.

Key words: space-based remote sensing, user portrait model, theme elements, intelligent recommendation

CLC Number:

P227

LONG En, Lü Shouye, CEN Pengrui, YANG Yuke, WEI Erlong, BAI Long. An intelligent recommendation method of multi-source remote sensing information considering user portrait[J]. Acta Geodaetica et Cartographica Sinica, 2023, 52(2): 297-306.

References

[1] 王密,杨芳.智能遥感卫星与遥感影像实时服务[J]. 测绘学报, 2019,48(12):1586-1594. DOI:10.11947/j.AGCS.2019.20190454. WANG Mi, YANG Fang. Intelligent remote sensing satellite and remote sensing image real-time service[J]. Acta Geodaetica et Cartographica Sinica, 2019,48(12):1586-1594. DOI:10.11947/j.AGCS.2019.20190454.
[2] 李德仁, 丁霖, 邵振峰. 面向实时应用的遥感服务技术[J]. 遥感学报, 2021, 25(1):15-24. LI Deren, DING Lin, SHAO Zhenfeng. Application-oriented real-time remote sensing service technology[J]. National Remote Sensing Bulletin, 2021, 25(1):15-24.
[3] 管清波, 冯书兴, 马彦华. 天基信息服务模式研究[J]. 装备学院学报, 2012, 23(6):66-70. GUAN Qingbo, FENG Shuxing, MA Yanhua. Research on mode of space-based intelligence service[J]. Journal of Academy of Equipment, 2012, 23(6):66-70.
[4] 李德仁, 沈欣. 我国天基信息实时智能服务系统发展战略研究[J]. 中国工程科学, 2020, 22(2):138-143. LI Deren, SHEN Xin. Research on the development strategy of real-time and intelligent space-based information service system in China[J]. Engineering Sciences, 2020, 22(2):138-143.
[5] PRUITT J, ADLIN T. The persona lifecycle[M]. New York:Morgan Kaufmann, 2006.
[6] 孙吉祥. 用户画像在推荐系统中的研究与应用[D].北京:北方工业大学, 2020. SUN Jixiang. Research and application of user profile in recommendation system[D]. Beijing:North China University of Technology, 2020.
[7] 马彬, 张文静, 谢显中. 面向终端个性化服务的模糊垂直切换算法[J].电子与信息学报, 2017, 39(6):1284-1290. MA Bin, ZHANG Wenjing, XIE Xianzhong. Individualization service oriented fuzzy vertical handover algorithm[J]. Journal of Electronics & Information Technology, 2017, 39(6):1284-1290.
[8] 成鹏飞, 黄钰譞, 刘正,等. 电子商务平台大数据个性化推荐方法研究[J]. 商学研究(双月刊), 2021, 28(3):116-124. CHENG Pengfei, HUANG Yuxuan, LIU Zheng, et al. Research on personalized recommendation method of big data on E-commerce platform[J]. Commercial Science Research, 2021,28(3):116-124.
[9] 刘建国, 周涛, 汪秉宏. 个性化推荐系统的研究进展[J].自然科学进展, 2009, 19(1):1-15. LIU Jianguo, ZHOU Tao, WANG Binghong. Progress of the personalized recommendation systems[J]. Progress of Nature and Science, 2009, 19(1):1-15.
[10] 李佳蓓. 基于多维度用户画像的高校图书资源个性化推荐系统构建研究[D].昆明:云南师范大学, 2021. LI Jiapei. Research on the construction of personalized recommendation system of college book resources based on multi-dimensional user portrait[D]. Kunming:Yunnan Normal University, 2021.
[11] 吴智勤, 李萍. 大数据情境下高校图书馆科研用户画像构建策略研究[J]. 江苏理工学院学报, 2019, 25(6):117-121. WU Zhiqing, LI Ping. Research on the researcher profiling construction strategy of university libraries in the big data situation[J]. Journal of Jiangsu University of Technology, 2019, 25(6):117-121.
[12] 李雅. 基于读者用户画像的高校图书馆精准化服务研究[J]. 农业图书情报学刊, 2018, 30(12):108-111. LI Ya. Research on precision service of university library based on user portrait[J]. Journal of Library and Information Sciences in Agriculture, 2018, 30(12):108-111.
[13] 叶凡, 孙玉, 陈崇成, 等. 基于地理标记照片的个性化景点推荐方法[J]. 地球信息科学学报, 2021, 23(8):1391-1400. YE Fan, SUN Yu, CHEN Chongcheng, et al. A personalized attraction recommendation method based on geotagged photos[J]. Journal of Geo-information Science, 2021, 23(8):1391-1400.
[14] 程滔. 大数据时代遥感数据管理与服务模式探索[J].国土资源遥感, 2016, 28(4):202-206. CHENG Tao. Exploring management and service mode for remote sensing data in big data era[J]. Remote Sensing for Land and Resources, 2016, 28(4):202-206.
[15] 李德仁, 张良培, 夏桂松. 遥感大数据自动分析与数据挖掘[J]. 测绘学报, 2014, 43(12):1211-126. LI Deren, ZHANG Liangpei, XIA Guisong. Automaic analysis and mining of remote sensing big data[J]. Acta Geodaetica et Cartographica Sinica, 2014, 43(12):1211-126.
[16] 刘晓飞, 朱斐, 伏玉琛, 等. 基于用户偏好特征挖掘的个性化推荐算法[J]. 计算机科学, 2020, 47(4):50-53. LIU Xiaofei, ZHU Fei, FU Yuchen, et al. Personalized recommendation algorithm based on user preference feature mining[J]. Computer Science, 2020, 47(4):50-53.
[17] 韩浩宇. 基于用户行为图的个性化推荐方法[D]. 合肥:中国科学技术大学, 2021. HAN Haoyu. User behavior graph based personalized recommendation methods[D]. Hefei:University of Science and Technology of China, 2021.
[18] 王泽根, 华一新. 主动空间信息服务技术研究[J]. 测绘学报, 2006, 35(4):379-384,389. WANG Zegen, HUA Yixin. Research on technology of active spatial information service[J]. Acta Geodaetica et Cartographica Sinica, 2006, 35(4):379-384,389.
[19] YU Chen, LI Zhenhong, BAI Lin, et al. Successful applications of generic atmospheric correction online service for InSAR (GACOS) to the reduction of atmospheric effects on InSAR observations[J]. Journal of Geodesy and Geoinformation Science, 2021,4(1):109-115.
[20] 牟乃夏, 刘文宝, 张灵先, 等. 空间信息服务的个性化问题[J].测绘科学, 2011,36(3):104-106. MOU Naixia, LIU Wenbao, ZHANG Lingxian, et al. Personalized recommendations on spatial information services[J]. Science of Surveying and Mapping, 2011, 36(3):104-106.
[21] 徐婷, 用户模型驱动的遥感信息智能服务方法[D]. 成都:成都理工大学, 2015.6. XU Ting. The intelligent service of spatial information based on user profile[D]. Chengdu:Chengdu University of Technology, 2015.6.
[22] 杨晓霞, 徐婷, 李少达, 等. 用户模型驱动的遥感信息智能服务方法[J], 测绘学报, 2015,44(11):1285-1294. DOI:10.11947/j.AGCS.2015.20140413. YANG Xiaoxia, Xu Ting, LI Shaoda, et al. A user profile-driven intelligent service of remote sensing information[J]. Acta Geodaetica et Cartographica Sinica, 2015,44(11):1285-1294. DOI:10.11947/j.AGCS.2015.20140413.
[23] 夏宇, 朱欣焰, 利用区间分析的空间信息智能分发决策[J].武汉大学学报(信息科学版), 2013,38(9):1103-1107. XIA Yu, ZHU Xinyan. Intelligent spatial information delivery decision-making by using interval analysis[J]. Geomatics and Information Science of Wuhan University, 2013,38(9):1103-1107.
[24] 夏宇. 面向空间信息智能分发的用户偏好模型研究[D]. 武汉:武汉大学, 2009. XIA Yu. The user profile model for intelligent delivery of spatial information[D]. Wuhan:Wuhan University, 2009.
[25] 李新广, 范明虎, 杜武. 面向空间信息智能分发的动态化用户偏好模型研究[J].测绘学报, 2011, 40(5):646-654. LI Xinguang, FAN Minghu, DU Wu. Research on dynamic user profile model for intelligent distribution of spatial information[J]. Acta Geodaetica et Cartographica Sinica, 2011, 40(5):646-654.