Paolo Viappiani
postdoctoral fellow - artificial intelligence group - university of toronto
Research Interests
My interests are within the following areas: recommender systems, constraint and planning systems, personal agents, preferences and user modeling, intelligent interfaces, machine learning, adaptive systems.
More in my research page.
current research
I am currently investigating preference reasoning and elicitation for automatic tools like personal agents or recommender systems. In our opinion, preference-elicitation should be open-ended allowing a diversity of modalites to acquire user models and target recommendations. Users should be allowed to state preferences in a way that is natural and convenient to them.
selected publications:
- Paolo Viappiani, Craig Boutilier. Regret-based Optimal Recommendation Sets in Conversational Recommender Systems. ACM Recommender Systems 2009 [PDF].
- Craig Boutilier, Kevin Regan, Paolo Viappiani. Online feature elicitation in interactive optimization. ICML 2009: 10 [PDF]
- Peintner, B.; Viappiani, P.; and Yorke-Smith, N. Preferences in Interactive Systems: Technical Challenges and Case Studies. AI Magazine 29(4), 13-24, Winter 2008. [PDF]
- Paolo Viappiani, Pearl Pu, Boi Faltings: Preference-based search with adaptive recommendations. AI Communications 21(2-3): 155-175 (2008) [PDF}
- P. Viappiani, B. Faltings and P. Pu. Preference-based Search using Example-Critiquing with Suggestions. Journal of Artificial Intelligence Research (JAIR), 27, 2006, pp. 465-503. [PDF]
latest news
Presenting a paper titled Optimal Recommendation Sets based on Regret at the IJCAI Workshop on Intelligent Techniques for Web Personalization and Recommender Systems.