中文    English

Journal of Library and Information Science in Agriculture ›› 2023, Vol. 35 ›› Issue (2): 30-44.doi: 10.13998/j.cnki.issn1002-1248.23-0085

Previous Articles     Next Articles

Multi-modal Characteristics Analysis and Customer Service Efficiency Improvement in the Digital Community Based on User Clustering

LI Canyao1, WEI Wei1, LIU Xiaoli1, ZHOU Linxing2,*, WANG Shuai2   

  1. 1. Guangxi China Tobacco Industry Co., Ltd., Nanning 530001;
    2. School of Cultural Heritage and Information Management, Shanghai University, Shanghai 200444
  • Received:2023-01-06 Published:2023-04-28

PDF (PC)

355

Abstract: [Purpose/Significance] Multi-modal feature analysis and service efficiency improvement of digital community consumers will help to provide a new vision for the construction of digital intelligent online communities and provide new impetus for relevant departments to make decisions. In addition, although the current research on digital consumption includes the relevant content of user analysis, it mainly aims at the formulation of detailed operation plans, and lacks the analysis of service efficiency improvement of digital communities. On the other hand, the research on user value orientation for online service quality optimization is mostly based on profile technology, which only considers the difference characteristics of a single target user, and lacks the horizontal comparison and difference attribution research of multi-modal features among groups. Based on this, this paper, from the perspective of value discovery, achieves clustering by aggregating user profiles, analyzes the multi-modal characteristics of consumer groups in digital communities, and proposes a service efficiency improvement scheme. [Method/process] First, this paper analyzed the target consumers in the digital community and established a cluster indicator system. Then, users were grouped, and the multi-modal information profile of the target group was restored based on group characteristics and inter-group interaction characteristics. Finally, it proposed the path to improve the efficiency of digital community services. In terms of technical implementation, the data related to consumer activities were extracted from the digital community, integrated, cleaned, and distributed to the storage bucket. The clustering indicator system was built through feature mining and existing indicators, and the indicators were mapped to aims, and DBSCAN clustering was carried out on the basis of using AP to realize the image. After grouping and naming, the characteristics analysis, interaction analysis, and drift and penetration phenomenon analysis were carried out according to the characteristics of various groups. We extracted various parameters of the design of digital community consumption activities, and built a decision variable function to find the optimal behavior equilibrium conditions of the digital product supplier, consumer and digital community. Based on this, we built an efficiency improvement tree, and proposed community service efficiency improvement strategies at the initial, middle and later stages of consumption activities. [Results/Conclusions] The empirical analysis results show that the model in this paper can first generate reasonable and effective clustering results, and then realize the classification of group characteristics and the analysis of inter-group infiltration and drift. The clustering results show six types of consumer groups: focus, center, special, sleeping, loss and general groups. Most groups will have user penetration, and only general user groups will have inter-group drift. The service efficiency improvement model shows that the most valued group is the center and key group. The inadequacy of this study is that the applicability of the model to multi-source heterogeneous data needs to be tested and there is still room for improvement in clustering granularity.

Key words: user clustering, AP-DBSCAN, multi-modal characteristics, digital community, digital consumption

CLC Number: 

  • G250
[1] 邓旻. 以消费者为中心的数字化营销策略研究[J]. 中国市场, 2022(27): 134-136.
DENG M.Research on consumer-centered digital marketing strategy[J]. China market, 2022(27): 134-136.
[2] 中国教育和科研计算机网. “十四五规划”纲要: 加快数字化发展建设数字中国[EB/OL].[2022-11-02]. https://www.edu.cn/xxh/zt/lhxxh/202103/t20210315_2084688.shtml.
[3] 陈雨倩, 孙虹, 葛王蓉. 数字经济下服装品牌营销策略研究[J]. 经营与管理, 2021(11): 51-55.
CHEN Y Q, SUN H, GE W R.Research on clothing brand marketing strategy in digital economy[J]. Management and administration, 2021(11): 51-55.
[4] 周毅, 白文琳. 公共信息服务政策内容的系统设计[J]. 情报理论与实践, 2013, 36(10): 10-15.
ZHOU Y, BAI W L.System design of public information service policy content[J]. Information studies: Theory & application, 2013, 36(10): 10-15.
[5] 胡振宇. 国内数字营销伦理乱象探因与治理研究——基于数字营销从业精英的访谈[J]. 当代传播, 2018(5): 80-84.
HU Z Y.A study on the causes and governance of ethical chaos in digital marketing in China -Based on an interview with digital marketing professionals[J]. Contemporary communication, 2018(5): 80-84.
[6] LI J, WU Y, XIAO J J.The impact of digital finance on household consumption: Evidence from China[J]. Economic modelling, 2020, 86: 317-326.
[7] 丁盈. “互联网+”背景下农产品数字营销策略研究[J]. 山西农经, 2022(14): 157-159.
DING Y.Research on digital marketing strategy of agricultural products under the background of "internet plus"[J]. Shanxi agricultural economy, 2022(14): 157-159.
[8] 郭褕亦. 泛媒体时代中文播客的数字营销——以小宇宙App为例[J]. 新媒体研究, 2021, 7(17): 47-49.
GUO C Y.Digital marketing of Chinese podcasts in pan-media era - A case study of small universe App[J]. New media research, 2021, 7(17): 47-49.
[9] 鲍磊, 江梓毓, 奚凯悦. 数字营销的发展:从1.0时代到4.0时代的进化[J]. 山东纺织经济, 2022, 39(2): 19-22.
BAO L, JIANG Z Y, XI K Y.Development of digital marketing: Evolution from era 1.0 to era 4.0[J]. Shandong textile economy, 2022,39(2): 19-22.
[10] 高培培. VR技术下数字营销专业虚拟仿真实训中心的建设与实践[J]. 营销界, 2022(1): 65-67.
GAO P P.Construction and practice of virtual simulation training center for digital marketing specialty under VR technology[J]. Marketing circles, 2022(1): 65-67.
[11] 苗月新. 基于营销宏观环境理论的网上零售影响因素分析[J]. 中国市场, 2022(5): 125-127.
MIAO Y X.Analysis on influencing factors of online retail based on marketing macro-environment theory[J]. China market, 2022(5): 125-127.
[12] DONG Y F, TAN R H, ZHANG P, et al.Product redesign using functional backtrack with digital twin[J]. Advanced engineering informatics, 2021, 49(3): 101.
[13] YOON S, MCCLEAN S T, CHAWLA N, et al.Working through an “infodemic”: The impact of COVID-19 news consumption on employee uncertainty and work behaviors[J]. Journal of applied psychology, 2021, 106(4): 501-517.
[14] CUMMINGS B.Digital assets add new concerns to estate planning[J]. Journal of financial planning, 2022, 35(8): 19.
[15] 徐婷. 基于新技术的数字营销战略——以AllSaints为例[J]. 内蒙古煤炭经济, 2020(22): 87-89.
XU T.Digital marketing strategy based on new technology - Taking AllSaints as an example[J]. Inner Mongolia coal economy, 2020(22): 87-89.
[16] KAPKAEVA N, GURZHIY A, MAYDANOVA S, et al.Digital platform for maritime port ecosystem: Port of hamburg case[J]. Transportation research procedia, 2021, 54: 909-917.
[17] 王佳. 数字化时代商业银行优化数字营销方式策略[J]. 中国市场, 2021(29): 119-120.
WANG J.Strategies for commercial banks to optimize digital marketing mode in the digital age[J]. China market, 2021(29): 119-120.
[18] 邓溱薇. 数字营销人才能力模型构建研究[D]. 杭州: 浙江工商大学, 2022.
DENG Z W.Research on the construction of competence model of digital marketing talent[D]. Hangzhou: Zhejiang Gongshang University, 2022.
[19] FUCHS C.The digital commons and the digital public sphere how to advance digital democracy today[J]. Westminster papers in communication and culture, 2021, 16(1): 9-26.
[20] SKARE M, SORIANO D.How globalization is changing digital technology adoption: An international perspective[J]. Journal of innovation & knowledge, 2021, 6(4): 222-233.
[21] 王宪朋. 基于视频大数据的用户画像构建[J]. 电视技术, 2017, 41(6): 20-23.
WANG X P.Construction of user portrait based on video big data[J]. Video engineering, 2017, 41(6): 20-23.
[22] 王帅. 突发公共卫生事件情境下在线健康社区用户画像与分群研究[J]. 情报科学, 2022, 40(6): 98-107.
WANG S.Study on user portrait and clustering of online health community in the context of public health emergencies[J]. Information science, 2022, 40(6): 98-107.
[23] 李旭光, 肖思琪, 李珊珊, 等基于知识行为的小米社区用户画像研究[J]. 农业图书情报学报, 2021, 33(8): 4-12.
LI X G, XIAO S Q, LI S S, et al.Research on user profiles of Xiaomi community based on knowledge behavior[J]. Journal of library and information science in agriculture, 2021, 33(8): 4-12.
[24] 曹树金, 岳文玉. 面向精准服务的图书馆用户画像研究[J]. 农业图书情报学报, 2021, 33(10): 4-19.
CAO S J, YUE W Y.Research on library user profiles for precision services[J]. Journal of library and information science in agriculture, 2021, 33(10): 4-19.
[25] LI D.Text mining model for virtual community user portrait based on social network analysis[J]. Tehnicki vjesnik - Technical gazette, 2019, 26(4): 1145-1151.
[26] MIN T, CAI W X.Portrait of decentralized application users: An overview based on large-scale Ethereum data[J]. CCF transactions on pervasive computing and interaction, 2022, 4: 124-141.
[27] 刘海鸥, 孙晶晶, 苏妍嫄, 等. 国内外用户画像研究综述[J]. 情报理论与实践, 2018, 41(11): 155-160.
LIU H O, SUN J J, SU Y Y, et al.Literature review of persona at home and abroad[J]. Information studies: Theory & application, 2018, 41(11): 155-160.
[28] 简宋全, 李青海, 秦于钦. 基于K-means的用户分群分析[J]. 现代计算机(专业版), 2017(29): 29-31.
JIAN S Q, LI Q H, QIN Y Q.User group analysis based on K-means[J]. Modern computer, 2017(29): 29-31.
[29] 占张帆. 基于用户分群的产品设计知识推送研究[D]. 南昌: 南昌航空大学, 2016.
ZHAN Z F.Research on product design knowledge push based on the user group[D]. Nanchang: Nanchang Hangkong University, 2016.
[30] 宋嵩. 基于URL分析的移动互联网用户分群[D]. 保定: 河北大学, 2013.
SONG S.Mobile Internet user clustering based on the analysis of URL[D]. Baoding: Hebei University, 2013.
[31] 吴艳玲, 孙思阳. 基于马尔可夫模型的图书馆用户聚类分群方法研究[J]. 情报科学, 2021, 39(11): 167-172.
WU Y L, SUN S Y.Clustering method of library users based on Markov model[J]. Information science, 2021, 39(11): 167-172.
[32] 王珗. 大数据时代网络营销策略分析[J]. 海峡科技与产业, 2015(12): 94-96.
WANG Y.Analysis of network marketing strategy in the era of big data[J]. Technology and industry across the straits, 2015(12): 94-96.
[33] 陈长庆. 在线旅行社客户关系管理研究——以途牛网为例[D]. 南京: 南京农业大学, 2017.
CHEN C Q.Research on customer relationship management of on-line travel agency - With Tuniu network as example[D]. Nanjing: Nanjing Agricultural University, 2017.
[34] 王婉, 张向先, 郭顺利, 等. 基于扎根理论的移动专业虚拟社区用户需求模型构建[J]. 情报科学, 2022, 40(6): 169-176.
WANG W, ZHANG X X, GUO S L, et al.Construction of user demand model of mobile professional virtual community based on grounded theory[J]. Information science, 2022, 40(6): 169-176.
[35] 张邦辉, 万秋兰, 吴健. 在线政务服务的营商环境优化效应探析——“数字红利”与“数字鸿沟”[J]. 中国行政管理, 2021(4): 70-75.
ZHANG B H, WAN Q L, WU J.Analysis of optimizing business climate by online government services: "Digital dividend" and "digital gap" effects[J]. Chinese public administration, 2021(4): 70-75.
[36] 于丽娟. “互联网+智慧服务”优化数字阅读精准推广模式研究[J]. 河南图书馆学刊, 2021, 41(9): 4-6.
YU L J.Research on "Internet+smart service" optimizing digital reading accurate promotion model[J]. The library journal of Henan
37 2021, 41(9): 4-6.
[37] 王毅, 吴睿青. 公共图书馆数字文化资源服务用户画像研究[J]. 图书情报工作, 2021, 65(16): 42-55.
WANG Y, WU R Q.Research on user portrait of digital cultural re-source service in public library[J]. Library and information service, 2021, 65(16): 42-55.
[38] CHEN Y B, HE J S, WEI W, et al.A multi-model approach for user portrait[J]. Future Internet, 2021, 13(6): 147.
[39] SUN Y, CHAI R Q.An early-warning model for online learners based on user portrait[J]. Ingénierie des systèmes d'information, 2020, 25(4): 535-541.
[40] WANG X, WEI X, MA J, et al.User portrait technology and its application scenario analysis[C] //BDE 2021: The 2021 3rd international conference on big data engineering, New York: Association for Computing Machinery, 2021: 64-69.
[41] 石光莲, 张敏, 郑伟伟. 形式概念分析在Folksonomy中的应用研究进展[J]. 图书情报工作, 2014, 58(9): 136-142.
SHI G L, ZHANG M, ZHENG W W.Advances in applied research of formal concept analysis in folksonomy[J]. Library and information service, 2014, 58(9): 136-142.
[42] 张淑萍. 长三角城市群国际消费中心城市竞争力评价[J]. 商业经济研究, 2022(10): 44-49.
ZHANG S P.An evaluation of international consumption center cities' competitiveness in Yangtze River Delta urban agglomeration[J]. Journal of commercial economics, 2022(10): 44-49.
[43] 王晖, 王晋立. 北京居民夜间消费倾向实证研究——基于Ⅳ-logit回归模型的分析[J]. 城市问题, 2021(2): 75-83.
WANG H, WANG J L.The empirical study on night consumption tendency of Beijing residents: Based on Ⅳ-logit regression model[J]. Urban problems, 2021(2): 75-83.
[44] 刘启林. 用户画像的基础、原理、方法论(模型)和应用[EB/OL]. [2022-11-06].https://zhuanlan.zhihu.com/p/140104236.
LIU Q L.The basis, principle, methodology (model) and application of user portrait[EB/OL]. [2022-11-06].https://zhuanlan.zhihu.com/p/140104236.
[45] LIN Z, CHAO M G.Research on acquisition on authors sources of sci-tech journals based on AARRR model[C] //ICCIR'21: Proceedings of the 2021 1st international conference on control and intelligent robotics, New York: Association for Computing Machinery, 2021: 193-196.
[46] 詹娟娟. 概率无向图模型近邻传播聚类算法的研究[D]. 南宁: 广西大学, 2017.
ZHAN J J.Research on affinity propagation clustering algorithm for probabilistic undirected graph model[D]. Nanning: Guangxi University, 2017.
[47] 李沛, 顾东蕾, 邹涛, 等. 基于社会网络分析的本科生社会科学潜在阅读兴趣小组挖掘探析——以中国药科大学为例[J]. 农业图书情报学报, 2021, 33(3): 78-89.
LI P, GU D L, ZOU T, et al.Exploration of potential reading interest groups in social sciences of undergraduates based on social network analysis: A case study of China pharmaceutical university[J]. Journal of library and information science in agriculture, 2021, 33(3): 78-89.
[48] 胡昌平, 胡吉明, 邓胜利. 基于Web2.0的用户群体交互分析及其服务拓展研究[J]. 中国图书馆学报, 2009, 35(5): 99-106.
HU C P, HU J M, DENG S L.Analysis of network users' group interaction and research for service based on the web 2.0[J]. Journal of library science in China, 2009, 35(5): 99-106.
[49] GASSE M, CHETELAT D, FERRONI N, et al.Exact combinatorial optimization with graph convolutional neural networks[C] //Advances in neural information processing systems 32(NeurIPS 2019), Vancouver Canada: MIT Press, 2019: 15580-15592.
[50] 孟秀丽, 吴一凡, 刘波. 考虑延误险的多期众包物流服务质量优化[J/OL]. 中国管理科学: 1-15[2022-11-08]. DOI:10.16381/j.cnki.issn1003-207x.2021.1807.
MENG X L, WU Y F, LIU B.Multi-phase crowdsourcing logistics service quality optimization considering delay insurance[J/OL]. China management science: 1-15[2022-11-08]. DOI:10.16381/j.cnki.issn1003-207x.2021.1807.
[51] NAGURNEY A, DANIELE P, SHUKLA S.A supply chain network game theory model of cybersecurity investments with nonlinear budget constraints[J]. Annals of operations research, 2017, 248(1/2): 405-427.
[1] XIAO Yun, XU Huanhuan, XIAO Yayuan, ZHAO Youlin, PANG Hangyuan. User Preference Mining in Digital Community Based on CLV Preference Mining Model [J]. Journal of Library and Information Science in Agriculture, 2023, 35(2): 45-60.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Editorial Board of Journal of Library and Information Science in Agriculture
Tel：(010)82109667　Fax：(010)82109667　E-mail：xuekan＠caas.cn
Powered by Beijing Magtech Co., Ltd.
知识共享许可协议