Discovering Virtual Interest Groups across Chat Rooms

Hua Li, Jeff Lau, Rafael Alonso

2012

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 military 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.

References

  1. Alonso, R. and Li, H.: Incremental user modeling with heterogeneous user behaviors, International Conference on Knowledge Management and Information Sharing 2010 (KMIS2010).
  2. Alonso, R. and Li, H.: Model-guided information discovery for intelligence analysis. Proceedings of the 14th ACM international conference on Information and knowledge management, ACM, Bremen, Germany, 2005.
  3. Alonso, R., Bloom, J. A., Li, H. and Basu, C.: An adaptive nearest neighbor search for a parts acquisition ePortal. Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, Washington, D.C., 2003.
  4. O'Connell, T. A., Grantham, J., Workman, K. and Wong W.: Editor-in-Chief's Corner: Leveraging GamePlaying Skills, Expectations and Behaviors of Digital Natives to Improve Visual Analytic Tools, Journal of Virtual Worlds Research, 2(1), ISSN: 1941-8477, April 2009.
  5. Terveen, L. and McDonald, D. W., 2005. Social Matching: A Framework and Research Agenda. ACM Trans. Comput. Hum. Interact. 12, 3 (Sep. 2005), 401- 434.
  6. Schleyer, T., Brian S. Butler, Mei Song, and Heiko Spallek, 2012. Conceptualizing and advancing research networking systems. ACM Trans. Comput.- Hum. Interact. 19, 1(May 2012).
Download


Paper Citation


in Harvard Style

Li H., Lau J. and Alonso R. (2012). Discovering Virtual Interest Groups across Chat Rooms . In Proceedings of the International Conference on Knowledge Management and Information Sharing - Volume 1: KMIS, (IC3K 2012) ISBN 978-989-8565-31-0, pages 152-157. DOI: 10.5220/0004131501520157


in Bibtex Style

@conference{kmis12,
author={Hua Li and Jeff Lau and Rafael Alonso},
title={Discovering Virtual Interest Groups across Chat Rooms},
booktitle={Proceedings of the International Conference on Knowledge Management and Information Sharing - Volume 1: KMIS, (IC3K 2012)},
year={2012},
pages={152-157},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004131501520157},
isbn={978-989-8565-31-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Knowledge Management and Information Sharing - Volume 1: KMIS, (IC3K 2012)
TI - Discovering Virtual Interest Groups across Chat Rooms
SN - 978-989-8565-31-0
AU - Li H.
AU - Lau J.
AU - Alonso R.
PY - 2012
SP - 152
EP - 157
DO - 10.5220/0004131501520157