Abstract
Versatile computerized aids for group argumentation for idea generation during problem solving process is becoming highlight research. In this paper, we propose a novel recommendation mechanism in the context of group argumentation, called On-demand Dynamic Recommendation Mechanism, which extracts user preferences from implicit and explicit feedbacks and provides personalized recommendation based on users’ utterances and demands. The valuable reference provided by the mechanism could be accumulated to form a knowledge immersion environment to satisfy ever changing demand of users in support of facilitating idea creativity during the process of group discussion.
Sponsored by China Postdoctoral Science Foundation.
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References
Adomavicius, G., Tuzhilin, A.: Personalization techiniques: a process-oriented perspective. Communication of the ACM 48, 83–90 (2005)
Cho, Y.H., Kim, J.K., Kim, S.H.: A personalized reommmender system based on Web usage mining and decision tree induction. Expert Systems with Applications 23(3), 329–342 (2002)
Domingos, P., Pazzani, M.: On the optimality of the simple Bayesian classifier under zero-one loss. Machine Learning 29, 103–130 (1997)
Liang, T.P., Lai, H.J.: Discovering user interests from web browsing behavior: an application to Internet news services. In: Proceedings of the 35th Annual Hawaii International Conference on System Sciences, Big Island, Hawaii, USA, pp. 203–212 (2002)
Liang, T.P., Yang, Y.F., Chen, D.N., Ku, Y.C.: A semantic-expansion Approach to Personalized Knowlege Recommendation. Decision Support Systems 45(3), 401–412 (2008)
McCallum, A., Nigam, K.: A comparison of event models for naive Bayes text classification. In: AAAI-1998 Workshop on Learning for Text Categorization. Tech. Rep. WS-98-05. AAAI Press, Menlo Park (1998)
Qian, X.S., Yu, J.Y., Dai, R.W.: A new Discipline of Science - the Study of Open Complex Giant System and its Methodology. Chinese Journal of Systems Engineering & Electronics 4(2), 2–12 (1993)
Sakagami, H., Kamba, T.: Learning personal preferences on online newspaper articles from user behaviors. Computer Networks and ISDN Systems 29, 1447–1455 (1997)
Sarwar, B., Karypis, G., Konstan, J.A., Riedl, J.: Analysis of recommendation algorithms for e-commerce. In: Proceedings of the ACM E-Commerce, pp. 158–167 (2000)
Sarwar, B., Karypis, G.: Item-based Collaborative Filtering Recommendation Algorithm. In: Proceedings of the 10th International World Wide Web Conference, Hong Kong, China (2001)
Tang, X.J.: Towards Meta-Synthetic Support to Unstructured Problem Solving. In: Chen, G.Y., et al. (eds.) Proceedings of the Fourth International Conference on Systems Science and Systems Engineering, pp. 203–209. Global-Link Publisher (2003)
Tang, X.J., Liu, Y.J., Zhang, W.: Computerized Support for Idea Generation During Knowledge Creating Process. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds.) KES 2005. LNCS (LNAI), vol. 3684, pp. 437–443. Springer, Heidelberg (2005)
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Xia, X., Zhou, X. (2011). On-Demand Dynamic Recommendation Mechanism in Support of Enhancing Idea Creativity for Group Argumentation. In: Hu, B., Liu, J., Chen, L., Zhong, N. (eds) Brain Informatics. BI 2011. Lecture Notes in Computer Science(), vol 6889. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23605-1_31
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DOI: https://doi.org/10.1007/978-3-642-23605-1_31
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