INCREMENTAL USER MODELING WITH HETEROGENEOUS USER BEHAVIORS

Rafael Alonso, Philip Bramsen, Hua Li

Abstract

We describe an innovative approach for incrementally learning user interests from multiple types of user behaviours or events. User interests are reflected in the concepts and their relations contained in these events. The concepts and relations form the structural elements of a user interest model. The relevance of each structural element is signified by a weight. Our user modeling algorithm builds dynamic user interest model with two concurrent processes. One process grows the model by intelligently incorporating concepts and relationships extracted from user events. Another process adapts the weights of these model elements by applying a novel combination of two mechanisms: reinforcement and forgetting, both important in modulating user interests. Our modeling algorithm supports incremental and real time modeling, and readily extends to new types of user events. One interesting application of user interest models is to identify a virtual interest group (VIG), which is an ordered set of other system users who exhibit interests similar to those of the target user. As a result, we can evaluate our user modeling algorithm through a VIG identification task. In a formative NIST evaluation using intelligence analysts, we achieved 95% VIG identification precision and recall.

References

  1. Alonso, R., Bloom, J.A., Li, H. and Basu, C., 2003. 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.
  2. Alonso, R. and Li, H., 2005a. Combating Cognitive Biases in Information Retrieval. in First International Conference on Intelligence Analysis Methods and Tools, McLean, VA, USA.
  3. Alonso, R. and Li, H., 2005b. Model-guided information discovery for intelligence analysis Proceedings of the 14th ACM international conference on Information and knowledge management, ACM, Bremen, Germany.
  4. Hijikata, Y., 2004. Implicit user profiling for on demand relevance feedback Proceedings of the 9th international conference on Intelligent user interfaces, ACM, Funchal, Madeira, Portugal.
  5. Guy, I., Ronen, I. and Wilcox, E., 2009. Do you know?: recommending people to invite into your social network Proceedings of the 13th international conference on Intelligent user interfaces, ACM, Sanibel Island, Florida, USA.
  6. Kelly, D. and Teevan, J., 2003. Implicit feedback for inferring user preference: a bibliography. SIGIR Forum, 37 (2). 18-28.
  7. Li, Y., Algarni, A., Wu, S.-T. and Xue, Y., 2009. Mining Negative Relevance Feedback for Information Filtering Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01, IEEE Computer Society.
  8. McDonald, D.W., 2003. Recommending collaboration with social networks: a comparative evaluation Proceedings of the SIGCHI conference on Human factors in computing systems, ACM, Ft. Lauderdale, Florida, USA.
  9. Mostafa, J., Mukhopadhyay, S., Palakal, M. and Lam, W., 1997. A multilevel approach to intelligent information filtering: model, system, and evaluation. ACM Trans. Inf. Syst., 15 (4). 368-399.
  10. Oard, D.W. and Kim, J., 2001. Modeling information content using observable behavior. in Proceedings of the 64th Annual Meeting of the American Society for Information Science and Technology, 38-45.
  11. Pazzani, M.J. and Billsus, D., 1997. Learning and Revising User Profiles: The Identification of Interesting Web Sites. Machine Learning, 27. 313- 331.
  12. Schroh, D., Bozowsky, N., Savigny, M. and Wright, W., 2009. nCompass Service Oriented Architecture for Tacit Collaboration Services Proceedings of the 2009 13th International Conference Information Visualisation - Volume 00, IEEE Computer Society.
  13. Shen, X., Tan, B. and Zhai, C., 2005. Implicit user modeling for personalized search Proceedings of the 14th ACM international conference on Information and knowledge management, ACM, Bremen, Germany.
  14. Teevan, J., Dumais, S.T. and Horvitz, E., 2005. Personalizing search via automated analysis of interests and activities Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, ACM, Salvador, Brazil.
  15. Wang, X., Fang, H. and Zhai, C., 2008. A study of methods for negative relevance feedback Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval, ACM, Singapore, Singapore.
  16. Wang, X., McCallum, A. and Wei, X., 2007. Topical Ngrams: Phrase and Topic Discovery, with an Application to Information Retrieval. in Proceedings of the 7th IEEE International Conference on Data Mining (ICDM).
  17. Zhang, J., Ackerman, M.S., Adamic, L. and Nam, K.K., 2007. QuME: a mechanism to support expertise finding in online help-seeking communities Proceedings of the 20th annual ACM symposium on User interface software and technology, ACM, Newport, Rhode Island, USA.
Download


Paper Citation


in Harvard Style

Alonso R., Bramsen P. and Li H. (2010). INCREMENTAL USER MODELING WITH HETEROGENEOUS USER BEHAVIORS . In Proceedings of the International Conference on Knowledge Management and Information Sharing - Volume 1: KMIS, (IC3K 2010) ISBN 978-989-8425-30-0, pages 129-135. DOI: 10.5220/0003062801290135


in Bibtex Style

@conference{kmis10,
author={Rafael Alonso and Philip Bramsen and Hua Li},
title={INCREMENTAL USER MODELING WITH HETEROGENEOUS USER BEHAVIORS},
booktitle={Proceedings of the International Conference on Knowledge Management and Information Sharing - Volume 1: KMIS, (IC3K 2010)},
year={2010},
pages={129-135},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003062801290135},
isbn={978-989-8425-30-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Knowledge Management and Information Sharing - Volume 1: KMIS, (IC3K 2010)
TI - INCREMENTAL USER MODELING WITH HETEROGENEOUS USER BEHAVIORS
SN - 978-989-8425-30-0
AU - Alonso R.
AU - Bramsen P.
AU - Li H.
PY - 2010
SP - 129
EP - 135
DO - 10.5220/0003062801290135