IEICE TRANSACTIONS on Information
Personalized Recommendation of Item Category Using Ranking on Time-Aware Graphs
Summary :
Recommendation systems have been widely used in E-commerce sites, social media and etc. An important recommendation task is to predict items that a user will perform actions on with users' historical data, which is called top-K recommendation. Recently, there is huge amount of emerging items which are divided into a variety of categories and researchers have argued or suggested that top-K recommendation of item category could be very beneficial for users to make better and faster decisions. However, the traditional methods encounter some common but crucial problems in this scenario because additional information, such as time, is ignored. The ranking algorithm on graphs and the increasingly growing amount of online user behaviors shed some light on these problems. We propose a construction method of time-aware graphs to use ranking algorithm for personalized recommendation of item category. Experimental results on real-world datasets demonstrate the advantages of our proposed method over competitive baseline algorithms.
- Publication
- IEICE TRANSACTIONS on Information Vol.E98-D No.4 pp.948-954
- Publication Date
- 2015/04/01
- Publicized
- 2015/01/19
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2014EDP7334
- Type of Manuscript
- PAPER
- Category
- Natural Language Processing
Authors
Chen CHEN
Nankai University
Chunyan HOU
Tianjin University of Technology
Peng NIE
Nankai University
Xiaojie YUAN
Nankai University
Keyword
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Chen CHEN, Chunyan HOU, Peng NIE, Xiaojie YUAN, "Personalized Recommendation of Item Category Using Ranking on Time-Aware Graphs" in IEICE TRANSACTIONS on Information,
vol. E98-D, no. 4, pp. 948-954, April 2015, doi: 10.1587/transinf.2014EDP7334.
Abstract: Recommendation systems have been widely used in E-commerce sites, social media and etc. An important recommendation task is to predict items that a user will perform actions on with users' historical data, which is called top-K recommendation. Recently, there is huge amount of emerging items which are divided into a variety of categories and researchers have argued or suggested that top-K recommendation of item category could be very beneficial for users to make better and faster decisions. However, the traditional methods encounter some common but crucial problems in this scenario because additional information, such as time, is ignored. The ranking algorithm on graphs and the increasingly growing amount of online user behaviors shed some light on these problems. We propose a construction method of time-aware graphs to use ranking algorithm for personalized recommendation of item category. Experimental results on real-world datasets demonstrate the advantages of our proposed method over competitive baseline algorithms.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2014EDP7334/_p
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@ARTICLE{e98-d_4_948,
author={Chen CHEN, Chunyan HOU, Peng NIE, Xiaojie YUAN, },
journal={IEICE TRANSACTIONS on Information},
title={Personalized Recommendation of Item Category Using Ranking on Time-Aware Graphs},
year={2015},
volume={E98-D},
number={4},
pages={948-954},
abstract={Recommendation systems have been widely used in E-commerce sites, social media and etc. An important recommendation task is to predict items that a user will perform actions on with users' historical data, which is called top-K recommendation. Recently, there is huge amount of emerging items which are divided into a variety of categories and researchers have argued or suggested that top-K recommendation of item category could be very beneficial for users to make better and faster decisions. However, the traditional methods encounter some common but crucial problems in this scenario because additional information, such as time, is ignored. The ranking algorithm on graphs and the increasingly growing amount of online user behaviors shed some light on these problems. We propose a construction method of time-aware graphs to use ranking algorithm for personalized recommendation of item category. Experimental results on real-world datasets demonstrate the advantages of our proposed method over competitive baseline algorithms.},
keywords={},
doi={10.1587/transinf.2014EDP7334},
ISSN={1745-1361},
month={April},}
Copy
TY - JOUR
TI - Personalized Recommendation of Item Category Using Ranking on Time-Aware Graphs
T2 - IEICE TRANSACTIONS on Information
SP - 948
EP - 954
AU - Chen CHEN
AU - Chunyan HOU
AU - Peng NIE
AU - Xiaojie YUAN
PY - 2015
DO - 10.1587/transinf.2014EDP7334
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E98-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 2015
AB - Recommendation systems have been widely used in E-commerce sites, social media and etc. An important recommendation task is to predict items that a user will perform actions on with users' historical data, which is called top-K recommendation. Recently, there is huge amount of emerging items which are divided into a variety of categories and researchers have argued or suggested that top-K recommendation of item category could be very beneficial for users to make better and faster decisions. However, the traditional methods encounter some common but crucial problems in this scenario because additional information, such as time, is ignored. The ranking algorithm on graphs and the increasingly growing amount of online user behaviors shed some light on these problems. We propose a construction method of time-aware graphs to use ranking algorithm for personalized recommendation of item category. Experimental results on real-world datasets demonstrate the advantages of our proposed method over competitive baseline algorithms.
ER -
English
