Number of co-authors:6
Number of publications with 3 favourite co-authors:Jie Zhang:3John Champaign:1Aaditeshwar Seth:1
Robin Cohen's 3 most productive colleagues in number of publications:Jie Zhang:10Thomas Tran:5Michael W. Fleming:2
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Publications by Robin Cohen (bibliography)
Champaign, John, Zhang, Jie and Cohen, Robin (2011): Coping with Poor Advice from Peers in Peer-Based Intelligent Tutoring: The Case of Avoiding Bad Annotations of Learning Objects. In: Proceedings of the 2011 Conference on User Modeling, Adaptation and Personalization 2011. pp. 38-49. Available online
In this paper, we examine a challenge that arises in the application of peer-based tutoring: coping with inappropriate advice from peers. We examine an environment where students are presented with those learning objects predicted to improve their learning (on the basis of the success of previous, like-minded students) but where peers can additionally inject annotations. To avoid presenting annotations that would detract from student learning (e.g. those found confusing by other students) we integrate trust modeling, to detect over time the reputation of the annotation (as voted by previous students) and the reputability of the annotator. We empirically demonstrate, through simulation, that even when the environment is populated with a large number of poor annotations, our algorithm for directing the learning of the students is effective, confirming the value of our proposed approach for student modeling. In addition, the research introduces a valuable integration of trust modeling into educational applications.
© All rights reserved Champaign et al. and/or their publisher
Seth, Aaditeshwar, Zhang, Jie and Cohen, Robin (2010): Bayesian Credibility Modeling for Personalized Recommendation in Participatory Media. In: Proceedings of the 2010 Conference on User Modeling, Adaptation and Personalization 2010. pp. 279-290. Available online
In this paper, we focus on the challenge that users face in processing messages on the web posted in participatory media settings, such as blogs. It is desirable to recommend to users a restricted set of messages that may be most valuable to them. Credibility of a message is an important criteria to judge its value. In our approach, theories developed in sociology, political science and information science are used to design a model for evaluating the credibility of messages that is user-specific and that is sensitive to the social network in which the user resides. To recommend new messages to users, we employ Bayesian learning, built on past user behaviour, integrating new concepts of context and completeness of messages inspired from the strength of weak ties hypothesis, from social network theory. We are able to demonstrate that our method is effective in providing the most credible messages to users and significantly enhances the performance of collaborative filtering recommendation, through a user study on the digg.com dataset.
© All rights reserved Seth et al. and/or their publisher
Zhang, Jie and Cohen, Robin (2006): Trusting advice from other buyers in e-marketplaces: the problem of unfair ratings. In: Fox, Mark S. and Spencer, Bruce (eds.) Proceedings of the 8th International Conference on Electronic Commerce - ICEC 2006 2006, Fredericton, New Brunswick, Canada. pp. 225-234. Available online
Cheng, Michael Y. K. and Cohen, Robin (2005): Reasoning About Interaction in a Multi-user System. In: Ardissono, Liliana, Brna, Paul and Mitrovic, Antonija (eds.) User Modeling 2005 - 10th International Conference - UM 2005 July 24-29, 2005, Edinburgh, Scotland, UK. pp. 189-198. Available online
Tran, Thomas and Cohen, Robin (2003): Modelling Reputation in Agent-Based Marketplaces to Improve the Performance of Buying Agents. In: Brusilovsky, Peter, Corbett, Albert T. and Rosis, Fiorella De (eds.) User Modeling 2003 - 9th International Conference - UM 2003 June 22-26, 2003, Johnstown, PA, USA. pp. 273-282. Available online
Fleming, Michael W. and Cohen, Robin (2001): A User Modeling Approach to Determining System Initiative in Mixed-Initiative AI Systems. In: Bauer, Mathias, Gmytrasiewicz, Piotr J. and Vassileva, Julita (eds.) User Modeling 2001 - 8th International Conference - UM 2001 July 13-17, 2001, Sonthofen, Germany. pp. 54-63. Available online
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