Social Network and Tag Sources Based Augmenting Collaborative Recommender System

DOI Web Site 被引用文献4件 参考文献20件
  • MA Tinghuai
    School of Computer, Nanjing University of Information Science & Technology Jiangsu Engineering Centre of Network Monitoring, Nanjing University of Information Science & Technology
  • ZHOU Jinjuan
    Jiangsu Engineering Centre of Network Monitoring, Nanjing University of Information Science & Technology
  • TANG Meili
    School of Public Administration, Nanjing University of Information Science & Technology
  • TIAN Yuan
    Computer Science Department, College of Computer and Information Science, King Saud University
  • AL-DHELAAN Abdullah
    Computer Science Department, College of Computer and Information Science, King Saud University
  • AL-RODHAAN Mznah
    Computer Science Department, College of Computer and Information Science, King Saud University
  • LEE Sungyoung
    Department of Computer Engineering, KyungHee University

説明

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.

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