IEICE TRANSACTIONS on Information
Social Network and Tag Sources Based Augmenting Collaborative Recommender System
Summary :
Recommender systems, which provide users with recommendations of content suited to their needs, have received great attention in today's online business world. However, most recommendation approaches exploit only a single source of input data and suffer from the data sparsity problem and the cold start problem. To improve recommendation accuracy in this situation, additional sources of information, such as friend relationship and user-generated tags, should be incorporated in recommendation systems. In this paper, we revise the user-based collaborative filtering (CF) technique, and propose two recommendation approaches fusing user-generated tags and social relations in a novel way. In order to evaluate the performance of our approaches, we compare experimental results with two baseline methods: user-based CF and user-based CF with weighted friendship similarity using the real datasets (Last.fm and Movielens). Our experimental results show that our methods get higher accuracy. We also verify our methods in cold-start settings, and our methods achieve more precise recommendations than the compared approaches.
- Publication
- IEICE TRANSACTIONS on Information Vol.E98-D No.4 pp.902-910
- Publication Date
- 2015/04/01
- Publicized
- 2014/12/26
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2014EDP7283
- Type of Manuscript
- PAPER
- Category
- Office Information Systems, e-Business Modeling
Authors
Tinghuai MA
Nanjing University of Information Science & Technology
Jinjuan ZHOU
Nanjing University of Information Science & Technology
Meili TANG
Nanjing University of Information Science & Technology
Yuan TIAN
King Saud University
Abdullah AL-DHELAAN
King Saud University
Mznah AL-RODHAAN
King Saud University
Sungyoung LEE
KyungHee University
Keyword
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Tinghuai MA, Jinjuan ZHOU, Meili TANG, Yuan TIAN, Abdullah AL-DHELAAN, Mznah AL-RODHAAN, Sungyoung LEE, "Social Network and Tag Sources Based Augmenting Collaborative Recommender System" in IEICE TRANSACTIONS on Information,
vol. E98-D, no. 4, pp. 902-910, April 2015, doi: 10.1587/transinf.2014EDP7283.
Abstract: Recommender systems, which provide users with recommendations of content suited to their needs, have received great attention in today's online business world. However, most recommendation approaches exploit only a single source of input data and suffer from the data sparsity problem and the cold start problem. To improve recommendation accuracy in this situation, additional sources of information, such as friend relationship and user-generated tags, should be incorporated in recommendation systems. In this paper, we revise the user-based collaborative filtering (CF) technique, and propose two recommendation approaches fusing user-generated tags and social relations in a novel way. In order to evaluate the performance of our approaches, we compare experimental results with two baseline methods: user-based CF and user-based CF with weighted friendship similarity using the real datasets (Last.fm and Movielens). Our experimental results show that our methods get higher accuracy. We also verify our methods in cold-start settings, and our methods achieve more precise recommendations than the compared approaches.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2014EDP7283/_p
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@ARTICLE{e98-d_4_902,
author={Tinghuai MA, Jinjuan ZHOU, Meili TANG, Yuan TIAN, Abdullah AL-DHELAAN, Mznah AL-RODHAAN, Sungyoung LEE, },
journal={IEICE TRANSACTIONS on Information},
title={Social Network and Tag Sources Based Augmenting Collaborative Recommender System},
year={2015},
volume={E98-D},
number={4},
pages={902-910},
abstract={Recommender systems, which provide users with recommendations of content suited to their needs, have received great attention in today's online business world. However, most recommendation approaches exploit only a single source of input data and suffer from the data sparsity problem and the cold start problem. To improve recommendation accuracy in this situation, additional sources of information, such as friend relationship and user-generated tags, should be incorporated in recommendation systems. In this paper, we revise the user-based collaborative filtering (CF) technique, and propose two recommendation approaches fusing user-generated tags and social relations in a novel way. In order to evaluate the performance of our approaches, we compare experimental results with two baseline methods: user-based CF and user-based CF with weighted friendship similarity using the real datasets (Last.fm and Movielens). Our experimental results show that our methods get higher accuracy. We also verify our methods in cold-start settings, and our methods achieve more precise recommendations than the compared approaches.},
keywords={},
doi={10.1587/transinf.2014EDP7283},
ISSN={1745-1361},
month={April},}
Copy
TY - JOUR
TI - Social Network and Tag Sources Based Augmenting Collaborative Recommender System
T2 - IEICE TRANSACTIONS on Information
SP - 902
EP - 910
AU - Tinghuai MA
AU - Jinjuan ZHOU
AU - Meili TANG
AU - Yuan TIAN
AU - Abdullah AL-DHELAAN
AU - Mznah AL-RODHAAN
AU - Sungyoung LEE
PY - 2015
DO - 10.1587/transinf.2014EDP7283
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E98-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 2015
AB - Recommender systems, which provide users with recommendations of content suited to their needs, have received great attention in today's online business world. However, most recommendation approaches exploit only a single source of input data and suffer from the data sparsity problem and the cold start problem. To improve recommendation accuracy in this situation, additional sources of information, such as friend relationship and user-generated tags, should be incorporated in recommendation systems. In this paper, we revise the user-based collaborative filtering (CF) technique, and propose two recommendation approaches fusing user-generated tags and social relations in a novel way. In order to evaluate the performance of our approaches, we compare experimental results with two baseline methods: user-based CF and user-based CF with weighted friendship similarity using the real datasets (Last.fm and Movielens). Our experimental results show that our methods get higher accuracy. We also verify our methods in cold-start settings, and our methods achieve more precise recommendations than the compared approaches.
ER -
English
