Cheng Zhaï et Fabio Crestani, deux chercheurs éminents dans le domaine de la recherche d'information

Fabio Crestani University of Lugano (USI) Switzerland

Title: A Personalised Recommendation System for Context-Aware Suggestions 

Abstract: The recently introduced TREC Contextual Suggestion track addresses the problem of suggesting contextually relevant places to a user visiting a new city based on his/her preferences and the location of the new city. In this talk I will frame the problem of representing and using context and will introduce a new and more sophisticated approach to constructing user profiles for that track in order to provide more accurate and relevant recommendations. The results show that our system not only significantly outperforms the TREC 2013 Contextual Suggestion track baseline method , but also performs very well in comparison to other runs submitted to that track, managing to achieve the best results in nearly half of all test contexts. 

Cheng Zhaï University of Illinois, USA

Title: Towards a Game-Theoretic Framework for Information Retrieval 

Abstract: The task of information retrieval (IR) has traditionally been defined as to rank a collection of documents in response to a query. While this definition has enabled most research progress in IR so far, it does not model accurately the actual retrieval task in a real IR application, where users tend to be engaged in an interactive process with multipe queries, and optimizing the overall performance of an IR system on an entire search session is far more important than its performance on an individual query. 
In this talk, I will present a new game-theoretic formulation of the IR problem where the key idea is to model information retrieval as a process of a search engine and a user playing a cooperative game, with a shared goal of satisfying the user's information need while minimizing the user's effort and the resource overhead on the retrieval system.  Such a game-theoretic framework offers several benefits. First, it naturally suggests optimization of the overall utility of an interactive retrieval system over a whole search session, thus breaking the limitation of the traditional formulation that optimizes ranking of documents for a single query. Second, it models the interactions between users and a search engine, and thus can optimize the collaboration of a search engine and its users, maximizing the "combined intelligence" of a system and users. Finally, it can potentially serve as a unified framework for optimizing both interactive information retrieval and active relevance judgment acquisition through crowdsourcing. I will discuss how the new framework can not only cover several emerging directions in current IR research as special cases, but also open up many interesting new research directions in IR.