Preference Elicitation


As people increasingly rely on interactive decision support systems to choose products and make decisions about tradeoffs, we must understand how interface technologies influence user behaviors. Crucial to this issue is to develop the mathematical decision theory into a new theory of decision information processing model, which shows how to present the most meaningful information in relation to users’ task goals and supporting context, and how to adaptively change the information content when goals evolve. Building effective interfaces for interactive decision support systems is challenging because 1) users’ preference models are incomplete and it is hard to elicit value functions for preferences that do not exist; 2) users’ beliefs about desirability are ephemeral, uncertain and context dependent; and 3) users have cognitive and emotional limitations for processing information. To develop a useful theory of decision information processing, we need to further understand the process by which humans make tradeoff decisions, how information affects this process, and how to construct effective interface technologies to augment user performance.


preference model, users’ beliefs, cognitive and emotional limitations


2003 – 2008


Li Chen and Pearl Pu


Swiss National Science Foundation


  • Li Chen and Pearl Pu. Survey of Preference Elicitation Methods. Technical Report No. IC/200467, Swiss Federal Institute of Technology in Lausanne (EPFL), Lausanne, Switzerland, pages 1-23, August 2004.
  • Pearl Pu and Li Chen. Integrating Tradeoff Support in Product Search Tools for E-Commerce Sites. In Proceeding of the ACM Conference on Electronic Commerce (EC’05), Vancouver, Canada, pages 269-278, June 5-8, 2005. (acceptance rate: 28%)
  • Li Chen and Pearl Pu. Trust Building in Recommender Agents. In Proceedings of the Workshop on Web Personalization, Recommender Systems and Intelligent User Interfaces at the 2nd International Conference on E-Business and Telecommunication Networks (ICETE’02) , Reading, UK, October 3-7, 2005, pp.135-145.
  • Pearl Pu and Li Chen. Trust Building with Explanation Interfaces. In Proceedings of the 11 th International Conference on Intelligent User Interface (IUI’06) , Sydney, Australia, pages 93-100, January 29-February 1, 2006. (acceptance rate: 24%)
  • Li Chen and Pearl Pu. Evaluating Critiquing-based Recommender Agents. In Proceedings of Twenty-first National Conference on Artificial Intelligence (AAAI-06), pages 157-162, Boston, USA, July 16-20, 2006. (acceptance rate: 21%)
  • Li Chen and Pearl Pu. Hybrid Critiquing-based Recommender Systems. In Proceedings of International Conference on Intelligent User Interfaces(IUI2007), pages 22-31, Hawaii, USA, January 28-31, 2007. (acceptance rate: 22%)
  • Li Chen and Pearl Pu(Best student paper award). Preference-Based Organization Interface: Aiding User Critiques in Recommender Systems. In Proceedings of International Conference on User Modeling (UM2007), pages 77-86, Corfu, Greece, June 25-29, 2007. (acceptance rate: 19.6%)
  • Pearl Pu and Li Chen. Trust-Inspiring Explanation Interfaces for Recommender Systems. Journal of Knowledge Based Systems, Elsevier Publishers, Volume 20, Issue 6, Pages 542-556, Auguest 2007.
  • Li Chen and Pearl Pu. The Evaluation of a Hybrid Critiquing System with Preference-based Recommendations Organization. In Proceedings of ACM Conference on Recommender Systems (RecSys’07), pages 169-172, Minneapolis, USA, October 19-20, 2007.
  • Pearl Pu, Li Chen and Pratyush Kumar. Evaluating Product Search and Recommender Systems for E-Commerce Environments. Electronic Commerce Research Journal, 8(1-2), June, pages 1-27, 2008.
  • Pearl Pu and Li Chen. User-Involved Preference Elicitation for Product Search and Recommender Systems. AI Magazine 29(4), pp. 93-103, Winter 2008. 
  • Li Chen and Pearl Pu. A Cross-Cultural User Evaluation of Product Recommender Interfaces. Proceedings of ACM Recommender Systems Conference, Lausanne, Switzerland, October 23-25, 2008.