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    Modeling Novelty in Multi-session Retrieval
    Update time: 2011-07-18
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    Time: 09:30am, July 18th, 2011 (Monday)
    Place: Meeting Hall, 4th Floor, ICT, CAS
    Speaker: Prof. Yiming Yang, Carnegie Mellon University 

    An open challenge in information retrieval is to detect the novel information from sequenced ranked lists, and to optimize system’s utility with respect to both relevance and novelty. Modeling novelty is difficult because novelty depends on user browsing history, and user behavior over ranked lists is non-deterministic. We propose a new probabilistic framework for stochastic modeling of user interactions with multi-session ranked lists, an algorithmic solution (based on sub-modularity) for efficient approximation of expected utility (an NP-hard problem), and new search strategies for retrieval optimization based on nugget detection and nugget-level relevance/novelty estimation. Our framework provides a strong foundation for new methodologies both in retrieval evaluation and in retrieval optimization. It allows significant utility improvements by leveraging realistic stochastic assumptions about user behavior, without requiring cost-intensive and time-consuming studies with human subjects. Our evaluations on benchmark datasets (TDT and TREC) show significant performance improvements with the proposed approach, over the results of other state-of-the-art methods. 

    Yiming Yang is a professor in the Language Technologies Institute and the Machine Learning Department in the School of Computer Science at Carnegie Mellon University. Her research has centered on statistical learning methods and their applications to a broad range of challenging problems, including large-scale text categorization, utility (relevance and novelty) based retrieval and adaptive filtering, personalization and active learning for recommendation systems, social network analysis for personalized email prioritization, etc.

    She received her Ph.D. in Computer Science from Kyoto University (Japan), and has been a faculty member at Carnegie Mellon University since 1996. More details can be found from her homepage http://www.cs.cmu.edu/~yiming/.

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