IEICE TRANSACTIONS on Information
Recommender System Using Implicit Social Information
Summary :
Social recommendation systems that make use of the user's social information have recently attracted considerable attention. These recommendation approaches partly solve cold-start and data sparsity problems and significantly improve the performance of recommendation systems. The essence of social recommendation methods is to utilize the user's explicit social connections to improve recommendation results. However, this information is not always available in real-world recommender systems. In this paper, a solution to this problem of explicit social information unavailability is proposed. The existing user-item rating matrix is used to compute implicit social information, and then an ISRec (implicit social recommendation algorithm) which integrates this implicit social information and the user-item rating matrix for social recommendation is introduced. Experimental results show that our method performs much better than state-of-the-art approaches; moreover, complexity analysis indicates that our approach can be applied to very large datasets because it scales linearly with respect to the number of observations in the matrices.
- Publication
- IEICE TRANSACTIONS on Information Vol.E98-D No.2 pp.346-354
- Publication Date
- 2015/02/01
- Publicized
- 2014/10/29
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2014EDP7174
- Type of Manuscript
- PAPER
- Category
- Data Engineering, Web Information Systems
Authors
Yusheng LI
Beijing University of Posts and Telecommunications
Meina SONG
Beijing University of Posts and Telecommunications
Haihong E
Beijing University of Posts and Telecommunications
Keyword
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Yusheng LI, Meina SONG, Haihong E, "Recommender System Using Implicit Social Information" in IEICE TRANSACTIONS on Information,
vol. E98-D, no. 2, pp. 346-354, February 2015, doi: 10.1587/transinf.2014EDP7174.
Abstract: Social recommendation systems that make use of the user's social information have recently attracted considerable attention. These recommendation approaches partly solve cold-start and data sparsity problems and significantly improve the performance of recommendation systems. The essence of social recommendation methods is to utilize the user's explicit social connections to improve recommendation results. However, this information is not always available in real-world recommender systems. In this paper, a solution to this problem of explicit social information unavailability is proposed. The existing user-item rating matrix is used to compute implicit social information, and then an ISRec (implicit social recommendation algorithm) which integrates this implicit social information and the user-item rating matrix for social recommendation is introduced. Experimental results show that our method performs much better than state-of-the-art approaches; moreover, complexity analysis indicates that our approach can be applied to very large datasets because it scales linearly with respect to the number of observations in the matrices.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2014EDP7174/_p
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@ARTICLE{e98-d_2_346,
author={Yusheng LI, Meina SONG, Haihong E, },
journal={IEICE TRANSACTIONS on Information},
title={Recommender System Using Implicit Social Information},
year={2015},
volume={E98-D},
number={2},
pages={346-354},
abstract={Social recommendation systems that make use of the user's social information have recently attracted considerable attention. These recommendation approaches partly solve cold-start and data sparsity problems and significantly improve the performance of recommendation systems. The essence of social recommendation methods is to utilize the user's explicit social connections to improve recommendation results. However, this information is not always available in real-world recommender systems. In this paper, a solution to this problem of explicit social information unavailability is proposed. The existing user-item rating matrix is used to compute implicit social information, and then an ISRec (implicit social recommendation algorithm) which integrates this implicit social information and the user-item rating matrix for social recommendation is introduced. Experimental results show that our method performs much better than state-of-the-art approaches; moreover, complexity analysis indicates that our approach can be applied to very large datasets because it scales linearly with respect to the number of observations in the matrices.},
keywords={},
doi={10.1587/transinf.2014EDP7174},
ISSN={1745-1361},
month={February},}
Copy
TY - JOUR
TI - Recommender System Using Implicit Social Information
T2 - IEICE TRANSACTIONS on Information
SP - 346
EP - 354
AU - Yusheng LI
AU - Meina SONG
AU - Haihong E
PY - 2015
DO - 10.1587/transinf.2014EDP7174
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E98-D
IS - 2
JA - IEICE TRANSACTIONS on Information
Y1 - February 2015
AB - Social recommendation systems that make use of the user's social information have recently attracted considerable attention. These recommendation approaches partly solve cold-start and data sparsity problems and significantly improve the performance of recommendation systems. The essence of social recommendation methods is to utilize the user's explicit social connections to improve recommendation results. However, this information is not always available in real-world recommender systems. In this paper, a solution to this problem of explicit social information unavailability is proposed. The existing user-item rating matrix is used to compute implicit social information, and then an ISRec (implicit social recommendation algorithm) which integrates this implicit social information and the user-item rating matrix for social recommendation is introduced. Experimental results show that our method performs much better than state-of-the-art approaches; moreover, complexity analysis indicates that our approach can be applied to very large datasets because it scales linearly with respect to the number of observations in the matrices.
ER -
English
