Resource recommendation in social annotation systems: A linear-weighted hybrid approach
作者: Jonathan GemmellThomas SchimolerBamshad MobasherRobin Burke
作者单位: 1Center for Web Intelligence, School of Computing, DePaul University, 243 South Wabash Avenue, Chicago, IL 60604, United States
刊名: Journal of Computer and System Sciences, 2012, Vol.78 (4), pp.1160-1174
来源数据库: Elsevier Journal
DOI: 10.1016/j.jcss.2011.10.006
关键词: Resource recommendationSocial annotation systemHybrid recommenders
原始语种摘要: Abstract(#br)Social annotation systems enable the organization of online resources with user-defined keywords. Collectively these annotations provide a rich information space in which users can discover resources, organize and share their finds, and connect to other users with similar interests. However, the size and complexity of these systems can lead to information overload and reduced utility for users. For these reasons, researchers have sought to apply the techniques of recommender systems to deliver personalized views of social annotation systems. To date, most efforts have concentrated on the problem of tag recommendation – personalized suggestions for possible annotations. Resource recommendation has not received the same systematic evaluation, in part because the task is...
全文获取路径: Elsevier  (合作)
影响因子:1 (2012)

  • recommendation 建议
  • annotation 注解
  • weighted 加权
  • resource 资源
  • users 使用者
  • social 群居的
  • extensibility 伸长性
  • complex 超群
  • connect 连接
  • varying 变化的