Paper Info
Title
Rightnow eservice center: internet customer service using a self-learning knowledge base
Abstract
Delivering effective customer service via the Internet requires attention to many aspects of knowledge management if it is to be convenient and satisfying for customers, while at the same time efficient and economical for the company or other organization. In RightNow eService Center, such management is enabled by automatically gathering meta-knowledge about the Answer documents held in the core knowledge base. A variety of AI techniques are used to facilitate the construction, maintenance, and navigation of the knowledge base. These include collaborative filtering, swarm intelligence, fuzzy logic, natural language processing, text clustering, and classification rule learning. Customers using eService Center report dramatic decreases in support costs and increases in customer satisfaction due to the ease of use provided by the "self-learning" features of the knowledge base.
Year
Venue
Keywords
2002
AAAI/IAAI
eservice center report,rightnow eservice center,internet customer service,customer satisfaction,knowledge base,knowledge management,classification rule learning,answer document,ai technique,effective customer service,core knowledge base,self-learning knowledge base,satisfiability,text clustering,diagnosis,fuzzy logic,computational complexity,natural language processing,ease of use,collaborative filtering,swarm intelligence
Field
DocType
ISBN
Customer intelligence,Customer satisfaction,Collaborative filtering,Computer science,Usability,Knowledge management,Artificial intelligence,Knowledge base,Core Knowledge,Machine learning,Open Knowledge Base Connectivity,The Internet
Conference
0-262-51129-0
Citations 
PageRank 
References 
1
0.39
6
Authors
4
Name
Order
Citations
PageRank
Stephen D. Durbin161.65
Doug Warner261.65
J. Neal Richter3254.38
Zuzana Gedeon441.22