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基于潜在主题的混合上下文推荐算法

李平 张路遥 曹霞 胡检华

李平, 张路遥, 曹霞, 胡检华. 基于潜在主题的混合上下文推荐算法[J]. 电子与信息学报, 2018, 40(4): 957-963. doi: 10.11999/JEIT170623
引用本文: 李平, 张路遥, 曹霞, 胡检华. 基于潜在主题的混合上下文推荐算法[J]. 电子与信息学报, 2018, 40(4): 957-963. doi: 10.11999/JEIT170623
LI Ping, ZHANG Luyao, CAO Xia, HU Jianhua. Hybrid Context Recommendation Algorithm Based on Latent Topic[J]. Journal of Electronics & Information Technology, 2018, 40(4): 957-963. doi: 10.11999/JEIT170623
Citation: LI Ping, ZHANG Luyao, CAO Xia, HU Jianhua. Hybrid Context Recommendation Algorithm Based on Latent Topic[J]. Journal of Electronics & Information Technology, 2018, 40(4): 957-963. doi: 10.11999/JEIT170623

基于潜在主题的混合上下文推荐算法

doi: 10.11999/JEIT170623
基金项目: 

湖南省教育厅资助重点项目(14A004)

Hybrid Context Recommendation Algorithm Based on Latent Topic

Funds: 

The Scientific Research Fund of Hunan Provincial Education Department (14A004)

  • 摘要: 针对单个环境上下文中项目访问记录稀疏的问题,推荐系统难以获取与当前环境上下文关联的用户偏好。该文设计了一种新的上下文关联性推荐(CTRR)算法。CTRR算法通过CTRR_LDA模型求解推荐项目出现在特定环境上下文的概率,并结合上下文后过滤推荐算法,对用户进行推荐。CTRR_LDA模型是在(LDA)模型的基础上,结合环境上下文和项目特征上下文,提出的项目与环境上下文的关联概率模型。该模型将环境上下文划分为多个环境上下文因子,每个环境上下文因子表示为K维的主题分布,挖掘环境上下文因子中项目出现的潜在主题特征。利用LDOS-CoMoDa网站上真实的电影数据集进行实验,验证了算法的可靠性。
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出版历程
  • 收稿日期:  2017-06-28
  • 修回日期:  2017-11-20
  • 刊出日期:  2018-04-19

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