Chin-Hui Lai, Duen-Ren Liu, Ya-Ting Chen


Recommender systems can mitigate the information overload problem and help workers retrieve knowledge based on their preferences. In a knowledge-intensive environment, knowledge workers need to access task-related codified knowledge (documents) to perform tasks. A worker’s document referencing behaviour can be modelled as a knowledge flow (KF) to represent the evolution of his/her information needs over time. Document recommendation methods can proactively support knowledge workers in the performance of tasks by recommending appropriate documents to meet their information needs. However, most traditional recommendation methods do not consider workers’ knowledge flows and the information needs of the majority of a group of workers with similar knowledge flows. A group’s needs may partially reflect the needs of an individual worker that cannot be inferred from his/her past referencing behaviour. Thus, we leverage the group perspective to complement the personal perspective by using a hybrid approach, which combines the KF-based group recommendation method (KFGR) with the user-based collaborative filtering method (UCF). The proposed hybrid method achieves a trade-off between the group-based and the personalized method by integrating the merits of both methods. Our experiment results show that the proposed method can enhance the quality of recommendations made by traditional methods.


  1. Baeza-Yates, R. & Ribeiro-Neto, B. 1999. Modern Information Retrieval, Boston, Addison-Wesley.
  2. Balabanovic, M. & Shoham, Y. 1997. Fab: content-based, collaborative recommendation. Communication of the ACM, 40, 66-72.
  3. Breese, J. S., Heckerman, D. & Kadie, C. 1998. Empirical Analysis of Predictive Algorithms for Collaborative Filtering. In: Proceedings of the Fourteenth Annual Conference on Uncertainty in Artificial Intelligence, 43-52.
  4. Herlocker, J. L., Konstan, J. A., Terveen, L. G. & Riedl, J. T. 2004. Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems (TOIS), 22, 5-53.
  5. Holz, H., Maus, H., Bernardi, A. & Rostanin, O. 2005. A lightweight approach for proactive, task-specific information delivery. In: Proceedings of the 5th International Conference on Knowledge Management (I-Know), 101-127.
  6. Jain, A. K., Murty, M. N. & Flynn, P. J. 1999. Data clustering: a review. ACM Computing Surveys (CSUR), 31, 264-323.
  7. Jameson, A. 2004. More than the sum of its members: challenges for group recommender systems. In: Proceedings of the working conference on Advanced visual interfaces, Gallipoli, Italy, 48-54.
  8. Kim, J. K., Kim, H. K., Oh, H. Y. & Ryu, Y. U. 2010. A group recommendation system for online communities. International Journal of Information Management, 30, 212-219.
  9. Konstan, J. A., Miller, B. N., Maltz, D., Herlocker, J. L., Gordon, L. R. & Riedl, J. 1997. GroupLens: applying collaborative filtering to Usenet news. Communications of the ACM, 40, 77-87.
  10. Lai, C. H. & Liu, D. R. 2009. Integrating Knowledge Flow Mining and Collaborative Filtering to Support Document Recommendation. Journal of Systems and Software, 82, 2023-2037.
  11. Liu, D. R., Wu, I. C. & Yang, K. S. 2005. Task-based KSupport system: disseminating and sharing taskrelevant knowledge. Expert Systems With Applications, 29, 408-423.
  12. Luo, X., Hu, Q., Xu, W. & Yu, Z. 2008. Discovery of Textual Knowledge Flow Based on the Management of Knowledge Maps. Concurrency and Computation: Practice and Experience, 20, 1791-1806.
  13. Mccarthy, J. F. & Anagnost, T. D. 1998. MusicFX: an arbiter of group preferences for computer supported collaborative workouts. In: Proceedings of the ACM conference on computer supported cooperative work (CSCW), Seattle, Washington, United States, 363-372.
  14. O'connor, M., Cosley, D., Konstan, J. A. & Riedl, J. 2001. PolyLens: a recommender system for groups of users. In: Proceedings of the seventh conference on European Conference on Computer Supported Cooperative Work, Bonn, Germany, 199-218.
  15. Oguducu, S. G. & Ozsu, M. T. 2006. Incremental clickstream tree model: Learning from new users for web page prediction. Distributed and Parallel Databases, 19, 5-27.
  16. Zhuge, H. 2002. A Knowledge Flow Model for Peer-topeer Team Knowledge Sharing and Management. Expert Systems With Applications, 23, 23-30.
  17. Zhuge, H. 2006. Discovery of Knowledge Flow in Science. Communications of the ACM, 49, 101-107.

Paper Citation

in Harvard Style

Lai C., Liu D. and Chen Y. (2011). RECOMMENDING DOCUMENTS VIA KNOWLEDGE FLOW-BASED GROUP RECOMMENDATION . In Proceedings of the 6th International Conference on Software and Database Technologies - Volume 2: ICSOFT, ISBN 978-989-8425-77-5, pages 341-349. DOI: 10.5220/0003486903410349

in Bibtex Style

author={Chin-Hui Lai and Duen-Ren Liu and Ya-Ting Chen},
booktitle={Proceedings of the 6th International Conference on Software and Database Technologies - Volume 2: ICSOFT,},

in EndNote Style

JO - Proceedings of the 6th International Conference on Software and Database Technologies - Volume 2: ICSOFT,
SN - 978-989-8425-77-5
AU - Lai C.
AU - Liu D.
AU - Chen Y.
PY - 2011
SP - 341
EP - 349
DO - 10.5220/0003486903410349