基于知识图谱的多粒度社交网络用户画像构建方法

doi:10.13229/j.cnki.jdxbgxb20210884

摘要/Abstract

摘要：

针对网络中社交用户属性信息划分不明确的问题，提出了一种基于知识图谱的多粒度社交用户画像构建方法。利用知识图谱映射用户信息，结合图谱分类划分映射数据多维度的属性类别，建立了社交用户集。赋予集合中每位用户对应属性标签的同等权重值，计算权重参数及兴趣标签频次，再根据用户点击频次信息，构建多粒度社交网络中用户的连续活动位置序列及活动范围序列，统一计算和划分序列中每位用户的特征，完成用户画像建立。仿真实验证明，本文方法用户画像构建时间不超过25 ms，对应属性匹配重叠数量最高为705，匹配度较高，说明所构建的用户画像信息划分较明确。

关键词: 计算机应用, 知识图谱, 社交用户, 高效映射, 多维度, 权重值

Abstract:

Aiming at the ambiguity of attribute information partition of social users in network， a multi-granularity social user image construction method based on knowledge graph was proposed. Using knowledge graph to map user information and classify multi-dimensional attributes of mapped data， a set of social users was established. The same weight value of each user's corresponding attribute tag in the set was given， and the weight parameters and the frequency of interest tag were calculated. Then， according to the click-frequency information of users， a sequence of continuous active locations and active ranges of users in a multi-granularity social network was constructed， and the features of each user in the sequence were calculated and divided in a unified manner to complete the establishment of user images. Simulation experiments show that the user profile construction time under the proposed method does not exceed 25 ms， and the maximum number of matching overlaps of corresponding attributes is 705. The matching degree is high， indicating that the constructed user profile information is clearly divided.

Key words: computer application, knowledge graph, social users, efficient mapping, multi-dimensions, weight values

中图分类号:

TP18

黎才茂,陈少凡,林成蓉,林昊,陈秋红. 基于知识图谱的多粒度社交网络用户画像构建方法[J]. 吉林大学学报(工学版), 2022, 52(12): 2947-2953.

Cai-mao LI,Shao-fan CHEN,Cheng-rong LIN,Hao LIN,Qiu-hong CHEN. Multi⁃grained social network user portrait construction method based on knowledge graph[J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(12): 2947-2953.

图/表 7

图1

图2

表1

图3

图4

图5

图6

参考文献 25

1	张艳丰,彭丽徽,洪闯,等. 因果要素关联视域下社交媒体倦怠用户画像模型构建[J]. 图书情报工作,2019, 63(14): 94-100.
	Zhang Yan-feng, Peng Li-hui, Hong Chuang, et al. Construction of user portrait model of social media burnout from the perspective of causal factor association[J]. Library and Information Service Work, 2019,63(14): 94-100.
2	陈烨,陈天雨,董庆兴. 多视角数据驱动的社会化问答平台用户画像构建模型研究[J]. 图书情报知识,2019, 37(5): 64-72.
	Chen Ye, Chen Tian-yu, Dong Qing-xing. Research on the model construction of multi-view-data-driven user profile for social Q&A platform[J]. Book and Intelligence Knowledge, 2019, 37(5): 64-72.
3	Ruan Q, Wu Q, Wang Y, et al. Effective learning model of user classification based on ensemble learning algorithms[J]. Computing,2019, 101(6): 531-545.
4	许力分,倪志伟,朱旭辉,等. 融合基于mapreduce并行改进二元蚁群算法与分形维数的属性选择方法[J]. 系统科学与数学, 2019, 39(6): 918-933.
	Xu Li-fen, Ni Zhi-wei, Zhu Xu-hui, et al. Attribute selection method combined with mapreduce-based improved BACO and fractal dimension[J]. Journal of Systems Science and Mathematical Sciences,2019,39(6):918-933.
5	刘小洋,唐婷,何道兵. 融合社交网络用户自身属性的信息传播数学建模与舆情演化分析[J]. 中文信息学报, 2019, 33(9): 115-122.
	Liu Xiao-yang, Tang Ting, He Dao-bing. Mathematical modeling and public opinion evolution analysis of information diffusion with the user attributes[J]. Journal of Chinese Information Processing,2019,33(9): 115-122.
6	李飞. 全渠道客户旅程体验图——基于用户画像、客户体验图和客户旅程图的整合研究[J]. 技术经济,2019,38(5):46-56.
	Li Fei. Omni-channel oustomer journey experience map: integration research based on personas, customer experience map and customer journey map[J]. Technology Economics, 2019, 38(5): 46-56.
7	郑源,陈品祥,冯学兵,等. 北京房地产新政下的房地产市场特点研究——基于不动产登记的用户画像[J]. 测绘通报,2019():176-180.
	Zheng Yuan, Chen Pin-xiang, Feng Xue-bing, et al. Real estate market characteristics under beijing new real estate policy——based on real estate registration[J]. Mapping Bulletin, 2019(Sup.2): 176-180.
8	孙铁柱,田琳. 基于CRT分类算法的用户画像分层模型——以银行借贷用户为例[J]. 情报科学, 2020,38(9): 75-81.
	Sun Tie-zhu, Tian Lin. Model of user layered profile based on CRT classification algorithm——taking loan users of bank as an example[J]. Intelligence Science,2020,38(9): 75-81.
9	张宜浩,朱小飞,徐传运,等. 基于用户评论的深度情感分析和多视图协同融合的混合推荐方法[J]. 计算机学报, 2019, 42(6): 1316-1333.
	Zhang Yi-hao, Zhu Xiao-fei, Xu Chuan-yun, et al. Hybrid recommendation approach based on deep sentiment analysis of user reviews and multi-view collaborative fusion[J]. Chinese Journal of Computers, 2019,42(6): 1316-1333.
10	张大勇,许磊,孔洪新. 社交媒体用户群体互动行为特征研究——以微信用户群分享为例[J]. 情报理论与实践, 2019, 42(10): 97-101, 116.
	Zhang Da-yong, Xu Lei, Kong Hong-xin. Users' interaction behavior characteristics of social media: an exploration of wechat group's sharing behavior[J]. Information Studies: Theory & Application, 2019, 42(10): 97-101, 116.
11	吴晓,王凌瑾,宁昱西,等. 从社交网络到地理网络——基于南京市高校新浪微博用户的分析[J]. 经济地理, 2020, 40(4): 83-95.
	Wu Xiao, Wang Ling-jin, Ning Yu-xi, et al. From social network to geographical network: a case study of college Sina weibo users in Nanjing[J]. Economic Geography,2020,40(4):83-95.
12	张敏军,华庆一. 基于概率矩阵分解算法的社交网络用户兴趣点个性化推荐[J].计算机科学,2020, 47(12):144-148.
	Zhang Min-jun, Hua Qing-yi. Personalized recommendation of social network users'interest points based on probability matrix decomposition algorithm[J]. Computer Science,2020,47(12):144-148.
13	杨玉仁,张书奎,龙浩,等. 群智感知中基于社交属性及有效用户计算的任务分发机制[J]. 计算机应用研究,2019,36(5):1493-1499.
	Yang Yu-ren, Zhang Shu-kui, Long Hao, et al. Task distribution mechanism based on social attribute and effective user calculation in crowd sensing [J]. Application Research of Computers,2019,36(5): 1493-1499.
14	周效章. 高校数字资源社会化服务的用户需求属性分析——基于Kano模型[J].情报杂志,2019,38(10):200-207.
	Zhou Xiao-zhang. Attributes of user's demand of the socialized digital resources service in universities: an analysis based on the Kano model[J]. Journal of Intelligence,2019,38(10):200-207.
15	张天杭,李婷婷,张永刚. 基于知识图谱嵌入的多跳中文知识问答方法[J]. 吉林大学学报:理学版, 2022, 60(1): 119-126.
	Zhang Tian-hang, Li Ting-ting, Zhang Yong-gang. Multi-hop chinese knowledge question answering method based on knowledge graph embedding[J]. Journal of Jilin University(Science Edition), 2022, 60(1): 119-126.
16	王美月,王萍,贾琼,等. 基于动态用户画像的学术虚拟社区粘性驱动机制研究[J]. 现代情报, 2019, 39(7): 9-17.
	Wang Mei-yue, Wang Ping, Jia Qiong, et al. Research on sticky driving mechanism in virtual academic community based on dynamic user profile[J]. Modern Intelligence,2019,39(7):9-17.
17	王凯,潘玮,杨枢,等. 基于模糊概念格的丁香园社区用户多粒度画像研究[J]. 情报理论与实践,2020,43(8):103-111.
	Wang Kai, Pan Wei, Yang Shu, et al. Multi-grained portrait of community users based on fuzzy concept lattice: taking ding xiang yuan as example [J].Information Studies:Theory & Application, 2020,43(8):103-111.
18	蒋明会,苗夺谦,罗晟,等. 基于粒计算的多粒度用户画像[J]. 模式识别与人工智能,2019,32(8):691-698.
	Jiang Ming-hui, Miao Duo-qian, Luo Sheng, et al. Multi-granularity user portrait based on granular computing[J]. Mode recognition and AI,2019,32(8): 691-698.
19	余明华,张治,祝智庭. 基于可视化学习分析的研究性学习学生画像构建研究[J]. 中国电化教育, 2020(12): 36-43.
	Yu Ming-hua, Zhang Zhi, Zhu Zhi-ting. Research on the construction of student portrait in research-based learning based on visual learning analytics[J]. China Educational Technology, 2020(12): 36-43.
20	康雁, 李涛, 李浩, 等. 融合知识图谱与协同过滤的推荐模型[J]. 计算机工程, 2020, 46(12): 73-79, 87.
	Kang Yan, Li Tao, Li Hao, et al, Recommendation model fusing with knowledge graph and collaborative filtering[J]. Computer Engineering, 2020, 46(12): 73-79, 87.
21	袁梅, 全太锋, 黄俊, 等. 基于卷积神经网络的烟雾检测[J]. 重庆邮电大学学报: 自然科学版, 2020, 32(4): 620-629.
	Yuan Mei, Quan Tai-feng, Huang Jun, et al. Smoke detection based on convolutional neural network[J]. Journal of Chongqing University of Posts and Telecommunications (Natural Science Edition), 2020, 32(4): 620-629.
22	胡静,陶洋.基于RPCA的群稀疏表示人脸识别方法[J]. 重庆邮电大学学报: 自然科学版, 2020, 32(3): 459-468.
	Hu Jing, Tao Yang. Group sparse representation face recognition method based on RPCA[J]. Journal of Chongqing University of Posts and Telecommunications (Natural Science Edition), 2020, 32(3): 459-468.
23	唐浩, 刘柏嵩, 刘晓玲, 等. 基于协同知识图谱特征学习的论文推荐方法[J]. 计算机工程, 2020, 46(9): 306-312.
	Tang Hao, Liu Bai-song, Liu Xiao-ling, et al. Paper recommendation method based on feature learning of collaborative knowledge graph[J]. Computer Engineering, 2020, 46(9): 306-312.
24	唐烨伟,茹丽娜,范佳荣,等. 基于学习者画像建模的个性化学习路径规划研究[J]. 电化教育研究,2019,40(10):53-60.
	Tang Ye-wei, Ru Li-na, Fan Jia-rong, et al. Research on planning of personalized learning path based on learner portrait modeling[J]. e-Education Research, 2019,40(10): 53-60.
25	李新春,马红艳,林森.基于局部邻域四值模式的掌纹掌脉融合识别[J].重庆邮电大学学报: 自然科学版, 2020, 32(4): 630-638.
	Li Xin-chun, Ma Hong-yan, Lin Sen. Palmprint and palm vein fusion recognition based on local neighbor quaternary pattern[J]. Journal of Chongqing University of Posts and Telecommunications (Natural Science Edition), 2021, 47(5): 285-291, 300.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

序号	群组数量	成员数量	社交话题	属性数量
1	2	55	电影	100
2	5	81	文学	100
3	4	128	创业	300
4	5	135	摄影	300
5	4	122	生活	200

[1]	祁贤雨,王巍,王琳,赵玉飞,董彦鹏. 基于物体语义栅格地图的语义拓扑地图构建方法[J]. 吉林大学学报(工学版), 2023, 53(2): 569-575.
[2]	时小虎,吴佳琦,吴春国,程石,翁小辉,常志勇. 基于残差网络的弯道增强车道线检测方法[J]. 吉林大学学报(工学版), 2023, 53(2): 584-592.
[3]	郭鹏,赵文超,雷坤. 基于改进Jaya算法的双资源约束柔性作业车间调度[J]. 吉林大学学报(工学版), 2023, 53(2): 480-487.
[4]	刘近贞,高国辉,熊慧. 用于脑组织分割的多尺度注意网络[J]. 吉林大学学报(工学版), 2023, 53(2): 576-583.
[5]	赵宏伟,张健荣,朱隽平,李海. 基于对比自监督学习的图像分类框架[J]. 吉林大学学报(工学版), 2022, 52(8): 1850-1856.
[6]	秦贵和,黄俊锋,孙铭会. 基于双手键盘的虚拟现实文本输入[J]. 吉林大学学报(工学版), 2022, 52(8): 1881-1888.
[7]	胡丹,孟新. 基于时变网格的对地观测卫星搜索海上船舶方法[J]. 吉林大学学报(工学版), 2022, 52(8): 1896-1903.
[8]	曲福恒,丁天雨,陆洋,杨勇,胡雅婷. 基于邻域相似性的图像码字快速搜索算法[J]. 吉林大学学报(工学版), 2022, 52(8): 1865-1871.
[9]	白天,徐明蔚,刘思铭,张佶安,王喆. 基于深度神经网络的诉辩文本争议焦点识别[J]. 吉林大学学报(工学版), 2022, 52(8): 1872-1880.
[10]	周丰丰,朱海洋. 基于三段式特征选择策略的脑电情感识别算法SEE[J]. 吉林大学学报(工学版), 2022, 52(8): 1834-1841.
[11]	周丰丰,张亦弛. 基于稀疏自编码器的无监督特征工程算法BioSAE[J]. 吉林大学学报(工学版), 2022, 52(7): 1645-1656.
[12]	王军,徐彦惠,李莉. 低能耗支持完整性验证的数据融合隐私保护方法[J]. 吉林大学学报(工学版), 2022, 52(7): 1657-1665.
[13]	康耀龙,冯丽露,张景安,陈富. 基于谱聚类的高维类别属性数据流离群点挖掘算法[J]. 吉林大学学报(工学版), 2022, 52(6): 1422-1427.
[14]	王文军,余银峰. 考虑数据稀疏的知识图谱缺失连接自动补全算法[J]. 吉林大学学报(工学版), 2022, 52(6): 1428-1433.
[15]	陈雪云,贝学宇,姚渠,金鑫. 基于G⁃UNet的多场景行人精确分割与检测[J]. 吉林大学学报(工学版), 2022, 52(4): 925-933.