Please wait a minute...
New Technology of Library and Information Service  2015, Vol. 31 Issue (11): 68-74    DOI: 10.11925/infotech.1003-3513.2015.11.10
Current Issue | Archive | Adv Search |
The Research of Spatial Measure of Users' Mental Model of Website Category from the View of Regional Differences
Qiang Shaohua1, Wu Peng2
1 School of Economics and Management, Nanjing TECH University, Nanjing 211816, China;
2 School of Economics and Management, Nanjing University of Science & Technology, Nanjing 210094, China
Download:
Export: BibTeX | EndNote (RIS)      
Abstract  

[Objective] This paper is to analyze the similarity between the organization structure of the website category and the user's subjective cognition directly based on the view of regional differences, which can support the website personalization.[Methods] Combined with the mental model theory and Web log mining method, this paper uses the website log data to obtain the user's cognition, and uses the multidimensional scaling to analyze the user's mental models of expected website category hierarchy from different regions.[Results] It is verified that there are differences in the mental models of the user's from different regions based on a Chinese e-commerce website case.[Limitations] In this paper, the test data is relatively small, and the new method needs to be verified by the more data.[Conclusions] The users' mental models of expected website category hierarchy are different according different regions. We can set up a personalized category hierarchy for users of different regions, which can better meet their use habits and improve their customer satisfactions.

Received: 22 May 2015      Published: 06 April 2016
:  TP393  
  G35  

Cite this article:

Qiang Shaohua, Wu Peng. The Research of Spatial Measure of Users' Mental Model of Website Category from the View of Regional Differences. New Technology of Library and Information Service, 2015, 31(11): 68-74.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.1003-3513.2015.11.10     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2015/V31/I11/68

[1] Geng X, Fan X, Bian J, et al. Optimizing User Exploring Experience in Emerging e-Commerce Products [C]. In: Proceedings of the 21st International Conference on World Wide Web. 2012: 23-32.
[2] Chen M, LaPaugh A S, Singh J P. Predicting Category Accesses for a User in a Structured Information Space[C]. In:Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 2002: 65-72.
[3] Schmutz P, Roth S P, Seckler M, et al. Designing Product Listing Pages—Effects on Sales and Users' Cognitive Workload [J]. International Journal of Human-Computer Studies, 2010, 68(7): 423-431.
[4] Noseworthy T J, Goode M R. Contrasting Rule-based and Similarity-based Category Learning: The Effects of Mood and Prior Knowledge on Ambiguous Categorization [J]. Journal of Consumer Psychology, 2011, 21(3): 362-371.
[5] Norman D A. Some Observations on Mental Models [A]. //Mental Models [M]. Lawrence Erlbaum Associates Inc, 1983: 7-14.
[6] 尤少伟, 吴鹏, 汤丽娟, 等. 基于路径搜索法的政府网站分类目录用户心智模型研究——以南京市政府网站为例[J]. 图书情报工作, 2012, 56(9): 129-135. (You Shaowei, Wu Peng, Tang Lijuan, et al. Users' Mental Model about Government Website Classification Catalogue Based on Pathfinder Method — Taking Nanjing Government Website as an Example [J]. Library and Information Service, 2012, 56(9): 129-135.)
[7] 李海涛, 宋琳琳. 用户使用网站的心智模型测量方法的选择及应用[J]. 情报理论与实践, 2015, 38(2): 11-16. (Li Haitao, Song Linlin. The Choice and Application of the Measuring Method of the User's Mental Model [J]. Information Studies: Theory & Application, 2015, 38(2): 11-16.)
[8] 张丽军. 基于日志挖掘的网站分类目录用户心智模型研究[D]. 南京: 南京理工大学, 2014. (Zhang Lijun. Research on Website Product Category Based on Mental Model by Data Mining [D]. Nanjing: Nanjing University of Science & Technolgoy, 2014.)
[9] 钱敏. 面向网站商品分类目录的用户心智模型测量研究[D]. 南京: 南京理工大学, 2013. (Qian Min. Research on the Measuring Method of the User's Mental Model Based on the Web Product Catalog [D]. Nanjing: Nanjing University of Science & Technolgoy, 2013.)
[10] Smith E E, Shoben E J, Rips L J. Structure and Process in Semantic Decisions: A Featural Model for Semantic Decisions [J]. Psychological Review, 1974, 81(3): 214-241.
[11] Gärdenfors P. Conceptual Spaces: The Geometry of Thought [M]. Cambridge, Mass: MIT Press, 2004.
[12] Nosofsky R M. Choice, Similarity, and the Context Theory of Classification [J]. Journal of Experimental Psychology: Learning, Memory, & Cognition, 1984, 10(1): 104-114.
[13] Ashby F G, Gott R E. Decision Rules in the Perception and Categorization of Multidimensional Stimuli [J]. Journal of Experimental Psychology: Learning, Memory, & Cognition, 1988, 14(1): 33-53.
[14] 吴鹏, 强韶华, 严明. 基于多维尺度法的网站分类目录理解用户心智模型空间性测量研究: 以政府网站为例[J]. 情报学报, 2012, 31(4): 436-448. (Wu Peng, Qiang Shaohua, Yan Ming. The Research on Spatial Measure of Users' Mental Model for Understanding Website Categories Based on Multidimensional Scaling: The Case of Government Websites [J]. Journal of the China Society for Scientific and Technical Information, 2012, 31(4): 436-448.)
[15] Coury B G, Weiland M Z, Cuqlock-Knopp V G. Probing the Mental Models of System State Categories with Multidi­mensional Scaling [J]. International Journal of Man-Machine Studies, 1992, 36(5): 673-696.

[1] Chen Jie,Ma Jing,Li Xiaofeng. Short-Text Classification Method with Text Features from Pre-trained Models[J]. 数据分析与知识发现, 2021, 5(9): 21-30.
[2] Li Wenna,Zhang Zhixiong. Research on Knowledge Base Error Detection Method Based on Confidence Learning[J]. 数据分析与知识发现, 2021, 5(9): 1-9.
[3] Sun Yu, Qiu Jiangnan. Research on Influence of Opinion Leaders Based on Network Analysis and Text Mining [J]. 数据分析与知识发现, 0, (): 1-.
[4] Wang Qinjie, Qin Chunxiu, Ma Xubu, Liu Huailiang, Xu Cunzhen. Recommending Scientific Literature Based on Author Preference and Heterogeneous Information Network[J]. 数据分析与知识发现, 2021, 5(8): 54-64.
[5] Li Wenna, Zhang Zhixiong. Entity Alignment Method for Different Knowledge Repositories with Joint Semantic Representation[J]. 数据分析与知识发现, 2021, 5(7): 1-9.
[6] Wang Hao, Lin Kerou, Meng Zhen, Li Xinlei. Identifying Multi-Type Entities in Legal Judgments with Text Representation and Feature Generation[J]. 数据分析与知识发现, 2021, 5(7): 10-25.
[7] Yang Hanxun, Zhou Dequn, Ma Jing, Luo Yongcong. Detecting Rumors with Uncertain Loss and Task-level Attention Mechanism[J]. 数据分析与知识发现, 2021, 5(7): 101-110.
[8] Xu Yuemei, Wang Zihou, Wu Zixin. Predicting Stock Trends with CNN-BiLSTM Based Multi-Feature Integration Model[J]. 数据分析与知识发现, 2021, 5(7): 126-138.
[9] Huang Mingxuan,Jiang Caoqing,Lu Shoudong. Expanding Queries Based on Word Embedding and Expansion Terms[J]. 数据分析与知识发现, 2021, 5(6): 115-125.
[10] Wang Xiwei,Jia Ruonan,Wei Yanan,Zhang Liu. Clustering User Groups of Public Opinion Events from Multi-dimensional Social Network[J]. 数据分析与知识发现, 2021, 5(6): 25-35.
[11] Ruan Xiaoyun,Liao Jianbin,Li Xiang,Yang Yang,Li Daifeng. Interpretable Recommendation of Reinforcement Learning Based on Talent Knowledge Graph Reasoning[J]. 数据分析与知识发现, 2021, 5(6): 36-50.
[12] Liu Tong,Liu Chen,Ni Weijian. A Semi-Supervised Sentiment Analysis Method for Chinese Based on Multi-Level Data Augmentation[J]. 数据分析与知识发现, 2021, 5(5): 51-58.
[13] Chen Wenjie,Wen Yi,Yang Ning. Fuzzy Overlapping Community Detection Algorithm Based on Node Vector Representation[J]. 数据分析与知识发现, 2021, 5(5): 41-50.
[14] Zhang Guobiao,Li Jie. Detecting Social Media Fake News with Semantic Consistency Between Multi-model Contents[J]. 数据分析与知识发现, 2021, 5(5): 21-29.
[15] Yan Qiang,Zhang Xiaoyan,Zhou Simin. Extracting Keywords Based on Sememe Similarity[J]. 数据分析与知识发现, 2021, 5(4): 80-89.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938   E-mail:jishu@mail.las.ac.cn