Authors:
Hua Li
;
Jeff Lau
and
Rafael Alonso
Affiliation:
SAIC and Inc., United States
Keyword(s):
User Modeling, Machine Learning, Virtual Interest Group, Chat, XMPP, IRC, Reinforcement Learning.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Communication, Collaboration and Information Sharing
;
Intelligent Information Systems
;
Knowledge Management and Information Sharing
;
Knowledge-Based Systems
;
Symbolic Systems
Abstract:
Chat has becoming an increasingly popular communication tool in our everyday life. When the number of related concurrent chat rooms gets large, tracking them 24x7 becomes very difficult. To address this research problem, we have developed VIGIR (Virtual Interest Group & Information Recommender), a tool for automatic chat room monitoring. The tool builds adaptive interest models for chat users, which are used to provide a number of personalized services including finding virtual interest groups (VIGs) for chat users. Dynamic identification of the VIG addresses the distributed user collaboration challenge, which is acute problem especially in military operations. VIGIR extends our prior work in user interest modeling into the domain of real-time text-based communications. We have evaluated the effectiveness of VIGIR in two studies. The first is a user-centred evaluation where we have achieved a precision at 60% and recall at 80% for VIG identification. In the second study using militar
y chat data, we have demonstrated an average precision of 45% to 50%. In addition, we have shown that the precision for predicting VIG increases over time as more data become available.
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