Open Access
The Bayesian Optimal Algorithm for Query Refinement in Information Retrieval
Yasunari Maeda,Fumitaro Goto,Hiroshi Masui,Fumito Masui,Masakiyo Suzuki +4 more
- 01 Jan 2011
TL;DR: This paper proposes an optimal algorithm for query refinement with reference to the Bayes criterion, which is a variant of query expansion that interactively recommends new terms related to the original query.
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Abstract: Summary To realize more efficient information retrieval it is critical to improve the user’s original query, because novice users can not be expected to formulate precise and effective queries. Queries can often be improved by adding extra terms that appear in relevant documents but which were not included in the original query. This is called query expansion. Query refinement, a variant of query expansion, interactively recommends new terms related to the original query. Because previous research did not offer any criterion to guarantee optimality, this paper proposes an optimal algorithm for query refinement with reference to the Bayes criterion.
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Citations
Matching Restaurant Menus to Crowdsourced Food Data: A Scalable Machine Learning Approach
Hesam Salehian,Patrick Howell,Chul Lee +2 more
- 13 Aug 2017
TL;DR: This work proposes a novel, practical, and scalable machine learning solution architecture, consisting of two major steps: a query generation approach, based on a Markov Decision Process algorithm, and a re-ranking step, using deep learning techniques, to meet the required matching quality goals.
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References
•Book
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Martin L. Puterman
- 15 Apr 1994
TL;DR: Puterman as discussed by the authors provides a uniquely up-to-date, unified, and rigorous treatment of the theoretical, computational, and applied research on Markov decision process models, focusing primarily on infinite horizon discrete time models and models with discrete time spaces while also examining models with arbitrary state spaces, finite horizon models, and continuous time discrete state models.
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Technical Note : \cal Q -Learning
Chris Watkins,Peter Dayan +1 more
TL;DR: This paper presents and proves in detail a convergence theorem forQ-learning based on that outlined in Watkins (1989), showing that Q-learning converges to the optimum action-values with probability 1 so long as all actions are repeatedly sampled in all states and the action- values are represented discretely.
Markov Decision Processes
P. Whittle,M. L. Puterman +1 more
TL;DR: Markov Decision Processes covers recent research advances in such areas as countable state space models with average reward criterion, constrained models, and models with risk sensitive optimality criteria, and explores several topics that have received little or no attention in other books.
Improving Retrieval Performance by Relevance Feedback
Gerard Salton,Chris Buckley +1 more
TL;DR: Relevance feedback is an automatic process, introduced over 20 years ago, designed to produce query formulations following an initial retrieval operation to demonstrate the effectiveness of the various methods.
Markov Decision Processes
Nicole Bäuerle,Ulrich Rieder +1 more
TL;DR: The theory of Markov Decision Processes is the theory of controlled Markov chains as mentioned in this paper, which has found applications in various areas like e.g. computer science, engineering, operations research, biology and economics.
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