IEICE TRANSACTIONS on Information
Predicting DataSpace Retrieval Using Probabilistic Hidden Information
Summary :
This paper discusses the issues involved in the design of a complete information retrieval system for DataSpace based on user relevance probabilistic schemes. First, Information Hidden Model (IHM) is constructed taking into account the users' perception of similarity between documents. The system accumulates feedback from the users and employs it to construct user oriented clusters. IHM allows integrating uncertainty over multiple, interdependent classifications and collectively determines the most likely global assignment. Second, Three different learning strategies are proposed, namely query-related UHH, UHB and UHS (User Hidden Habit, User Hidden Background, and User Hidden keyword Semantics) to closely represent the user mind. Finally, the probability ranking principle shows that optimum retrieval quality can be achieved under certain assumptions. An optimization algorithm to improve the effectiveness of the probabilistic process is developed. We first predict the data sources where the query results could be found. Therefor, compared with existing approaches, our precision of retrieval is better and do not depend on the size and the DataSpace heterogeneity.
- Publication
- IEICE TRANSACTIONS on Information Vol.E93-D No.7 pp.1991-1994
- Publication Date
- 2010/07/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E93.D.1991
- Type of Manuscript
- LETTER
- Category
- Data Engineering, Web Information Systems
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Keyword
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Gile Narcisse FANZOU TCHUISSANG, Ning WANG, Nathalie Cindy KUICHEU, Francois SIEWE, De XU, Shuoyan LIU, "Predicting DataSpace Retrieval Using Probabilistic Hidden Information" in IEICE TRANSACTIONS on Information,
vol. E93-D, no. 7, pp. 1991-1994, July 2010, doi: 10.1587/transinf.E93.D.1991.
Abstract: This paper discusses the issues involved in the design of a complete information retrieval system for DataSpace based on user relevance probabilistic schemes. First, Information Hidden Model (IHM) is constructed taking into account the users' perception of similarity between documents. The system accumulates feedback from the users and employs it to construct user oriented clusters. IHM allows integrating uncertainty over multiple, interdependent classifications and collectively determines the most likely global assignment. Second, Three different learning strategies are proposed, namely query-related UHH, UHB and UHS (User Hidden Habit, User Hidden Background, and User Hidden keyword Semantics) to closely represent the user mind. Finally, the probability ranking principle shows that optimum retrieval quality can be achieved under certain assumptions. An optimization algorithm to improve the effectiveness of the probabilistic process is developed. We first predict the data sources where the query results could be found. Therefor, compared with existing approaches, our precision of retrieval is better and do not depend on the size and the DataSpace heterogeneity.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E93.D.1991/_p
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@ARTICLE{e93-d_7_1991,
author={Gile Narcisse FANZOU TCHUISSANG, Ning WANG, Nathalie Cindy KUICHEU, Francois SIEWE, De XU, Shuoyan LIU, },
journal={IEICE TRANSACTIONS on Information},
title={Predicting DataSpace Retrieval Using Probabilistic Hidden Information},
year={2010},
volume={E93-D},
number={7},
pages={1991-1994},
abstract={This paper discusses the issues involved in the design of a complete information retrieval system for DataSpace based on user relevance probabilistic schemes. First, Information Hidden Model (IHM) is constructed taking into account the users' perception of similarity between documents. The system accumulates feedback from the users and employs it to construct user oriented clusters. IHM allows integrating uncertainty over multiple, interdependent classifications and collectively determines the most likely global assignment. Second, Three different learning strategies are proposed, namely query-related UHH, UHB and UHS (User Hidden Habit, User Hidden Background, and User Hidden keyword Semantics) to closely represent the user mind. Finally, the probability ranking principle shows that optimum retrieval quality can be achieved under certain assumptions. An optimization algorithm to improve the effectiveness of the probabilistic process is developed. We first predict the data sources where the query results could be found. Therefor, compared with existing approaches, our precision of retrieval is better and do not depend on the size and the DataSpace heterogeneity.},
keywords={},
doi={10.1587/transinf.E93.D.1991},
ISSN={1745-1361},
month={July},}
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TY - JOUR
TI - Predicting DataSpace Retrieval Using Probabilistic Hidden Information
T2 - IEICE TRANSACTIONS on Information
SP - 1991
EP - 1994
AU - Gile Narcisse FANZOU TCHUISSANG
AU - Ning WANG
AU - Nathalie Cindy KUICHEU
AU - Francois SIEWE
AU - De XU
AU - Shuoyan LIU
PY - 2010
DO - 10.1587/transinf.E93.D.1991
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E93-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2010
AB - This paper discusses the issues involved in the design of a complete information retrieval system for DataSpace based on user relevance probabilistic schemes. First, Information Hidden Model (IHM) is constructed taking into account the users' perception of similarity between documents. The system accumulates feedback from the users and employs it to construct user oriented clusters. IHM allows integrating uncertainty over multiple, interdependent classifications and collectively determines the most likely global assignment. Second, Three different learning strategies are proposed, namely query-related UHH, UHB and UHS (User Hidden Habit, User Hidden Background, and User Hidden keyword Semantics) to closely represent the user mind. Finally, the probability ranking principle shows that optimum retrieval quality can be achieved under certain assumptions. An optimization algorithm to improve the effectiveness of the probabilistic process is developed. We first predict the data sources where the query results could be found. Therefor, compared with existing approaches, our precision of retrieval is better and do not depend on the size and the DataSpace heterogeneity.
ER -
English
