Modeling Expected Utility of Multi-session Information Distillation
- Cite this paper as:
- Yang Y., Lad A. (2009) Modeling Expected Utility of Multi-session Information Distillation. In: Azzopardi L. et al. (eds) Advances in Information Retrieval Theory. ICTIR 2009. Lecture Notes in Computer Science, vol 5766. Springer, Berlin, Heidelberg
An open challenge in information distillation is the evaluation and optimization of the utility of ranked lists with respect to flexible user interactions over multiple sessions. Utility depends on both the relevance and novelty of documents, and the novelty in turn depends on the user interaction history. However, user behavior is non-deterministic. We propose a new probabilistic framework for stochastic modeling of user behavior when browsing multi-session ranked lists, and a novel approximation method for efficient computation of the expected utility over numerous user-interaction patterns. Using this framework, we present the first utility-based evaluation over multi-session search scenarios defined on the TDT4 corpus of news stories, using a state-of-the-art information distillation system. We demonstrate that the distillation system obtains a 56.6% utility enhancement by combining multi-session adaptive filtering with novelty detection and utility-based optimization of system parameters for optimal ranked list lengths.
KeywordsMulti-session distillation utility evaluation based both on novelty and relevance stochastic modeling of user browsing behavior
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