Mining Proximal Social Network Intelligence for Quality Decision Support

The concepts of proximity have been utilized for exploring both psychological and geographical incentives for users within social networks to collaborate with others for mutual goals. The massive information does not facilitate quality decision support. In this paper, we focus on mining the proximal social network intelligence for quality decision support. The utilization of investigating both the context and the content of the application domain from social network relationships would highly improve the information quality for better decisions. Mining proximal social network intelligence from both context and content enable quality decision making. We illustrate a case of leisure recommendation e-service for bicycle exercise entertainment in Taiwan. We introduce the proximity e-service as well as its theoretical support.The most recent personalized experience according to its context provides remarkable perceptual data from unique information sources. Moreover, the social network relationships extend the power of the unique perceptual information to converge as the collective social network intelligence.