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Social Network and Tag Sources Based Augmenting Collaborative Recommender System
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- MA Tinghuai
- School of Computer, Nanjing University of Information Science & Technology Jiangsu Engineering Centre of Network Monitoring, Nanjing University of Information Science & Technology
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- ZHOU Jinjuan
- Jiangsu Engineering Centre of Network Monitoring, Nanjing University of Information Science & Technology
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- TANG Meili
- School of Public Administration, Nanjing University of Information Science & Technology
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- TIAN Yuan
- Computer Science Department, College of Computer and Information Science, King Saud University
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- AL-DHELAAN Abdullah
- Computer Science Department, College of Computer and Information Science, King Saud University
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- AL-RODHAAN Mznah
- Computer Science Department, College of Computer and Information Science, King Saud University
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- LEE Sungyoung
- Department of Computer Engineering, KyungHee University
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Description
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.
Journal
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- IEICE Transactions on Information and Systems
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IEICE Transactions on Information and Systems E98.D (4), 902-910, 2015
The Institute of Electronics, Information and Communication Engineers
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Details 詳細情報について
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- CRID
- 1390282679354328448
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- NII Article ID
- 130005061850
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- ISSN
- 17451361
- 09168532
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- Text Lang
- en
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- Data Source
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- JaLC
- Crossref
- CiNii Articles
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- Abstract License Flag
- Disallowed