可视化潜在社区:一种多智能体方法

Sen Yoshida, Koji Kamei, M. Yokoo, Takeshi Ohguro, Kaname Funakoshi, F. Hattori
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引用次数: 7

摘要

通用术语communityware (M. Chalmers和P. Chitson, 1992)被提议用来表示通常支持开放网络(如Internet或移动计算系统)上非正式群体的形成、活动和组织的系统。Communityware旨在为还没有明确定义的目标的各种各样的人提供支持。我们解释了一个示例社区软件系统的开发,该系统图形化地表示了一个潜在的社区,即共享共同兴趣的人们。该系统确定了每对人的共同兴趣程度,然后将用户定位在一个平面上,用户之间的距离反映了他们之间的相关性。实现该系统的一个主要挑战是区分一组适当的关键字来可视化潜在社区。系统从用户的配置文件中提取关键字,并计算使用这些关键字的用户之间的关联度。我们使用关键字的权重系统,以及从用户反馈中确定所需权重的学习机制。研究表明,智能体可以通过合作加速这一学习过程。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Visualizing potential communities: a multiagent approach
The generic term communityware (M. Chalmers and P. Chitson, 1992) has been proposed to represent systems that typically support the formation, activities, and organization of informal groups on open networks such as the Internet or mobile computing systems. Communityware is intended to provide support for a diverse and amorphous group of people who do not yet have a clearly defined goal. We explain the development of an example communityware system that graphically presents a potential community, i.e., people sharing common interests. The proposed system determines the degree of common interests for each pair of people, and it then locates the users in a plane where the distance between the users reflects the relevance between them. A major challenge for implementing this system is distinguishing an appropriate set of keywords to visualize potential communities. The system extracts keywords from the users' profiles and calculates the degree of relevance among the users using those keywords. We use a weight system for keywords as well as a learning mechanism that determines the desired weight from user feedback. It is shown that agents can speed up this learning process by cooperation.
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