NDLI: Mining user session data to facilitate user interaction with a customer service knowledge base in RightNow Web

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Mining user session data to facilitate user interaction with a customer service knowledge base in RightNow Web

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Mining user session data to facilitate user interaction with a customer service knowledge base in RightNow Web

Content Provider	ACM Digital Library
Author	Richter, J. Neal Durbin, Stephen D. Banerjee, Bikramjit Warner, Doug
Abstract	RightNow Web is an integrated software package for web-based customer service that has, at its core, a database of answers to frequently asked questions (FAQs). One major design goal is to facilitate end-user interaction with this dynamic document collection, i.e. make it as easy and efficient as possible for users to browse the collection and locate desired information. To this end, we perform several types of analysis on the session tracking database that records user navigation histories. First, using both explicit and implicit measures of user satisfaction, we infer a "solved count" representing the average utility of an FAQ. Second, using the user navigation patterns we construct a link matrix representing connections between FAQs. The technique of building up the link matrix and using it to advise users on related information amounts to a form of the "swarm intelligence" method of finding optimal paths. Both solved count and the link matrix are continuously updated as users interact with the site; furthermore, they are periodically "aged" to emphasize recent activity. The synergistic combination of these techniques allows users to learn from the database in a more effective manner, as evidenced by usage statistics.
Starting Page	467
Ending Page	472
Page Count	6
File Format	PDF
ISBN	158113391X
DOI	10.1145/502512.502583
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2001-08-26
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	User session User data Self-help Collaborative filtering Multi-agent system Customer service Clustering
Content Type	Text
Resource Type	Article

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