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A Statistical Model for User Preference
June 2005 (vol. 17 no. 6)
pp. 834-843
ASCII Text
x 
 
Sung Young Jung, Jeong-Hee Hong, Taek-Soo Kim, "A Statistical Model for User Preference," IEEE Transactions on Knowledge and Data Engineering, vol. 17, no. 6, pp. 834-843, June, 2005.
 
 
BibTex
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@article{ 10.1109/TKDE.2005.86,
author = {Sung Young Jung and Jeong-Hee Hong and Taek-Soo Kim},
title = {A Statistical Model for User Preference},
journal ={IEEE Transactions on Knowledge and Data Engineering},
volume = {17},
number = {6},
issn = {1041-4347},
year = {2005},
pages = {834-843},
doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2005.86},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Knowledge and Data Engineering
TI - A Statistical Model for User Preference
IS - 6
SN - 1041-4347
SP834
EP843
EPD - 834-843
A1 - Sung Young Jung,
A1 - Jeong-Hee Hong,
A1 - Taek-Soo Kim,
PY - 2005
KW - Personalization
KW - user preference
KW - recommendation
KW - user modeling
KW - text mining
KW - mutual information
KW - data sparseness
KW - feature-combining weight
KW - Pareto distribution.
VL - 17
JA - IEEE Transactions on Knowledge and Data Engineering
ER -
 
Modeling user preference is one of the challenging issues in intelligent information systems. Extensive research has been performed to automatically analyze user preference and to utilize it. One problem still remains: The representation of preference, usually given by measure of vector similarity or probability, does not always correspond to common sense of preference. This problem gets worse in the case of negative preference. To overcome this problem, this paper presents a preference model using mutual information in a statistical framework. This paper also presents a method that combines information of joint features and alleviates problems arising from sparse data. Experimental results, compared with the previous recommendation models, show that the proposed model has the highest accuracy in recommendation tests.

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Index Terms:
Personalization, user preference, recommendation, user modeling, text mining, mutual information, data sparseness, feature-combining weight, Pareto distribution.
Citation:
Sung Young Jung, Jeong-Hee Hong, Taek-Soo Kim, "A Statistical Model for User Preference," IEEE Transactions on Knowledge and Data Engineering, vol. 17, no. 6, pp. 834-843, June 2005, doi:10.1109/TKDE.2005.86
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