计算机科学 ›› 2015, Vol. 42 ›› Issue (8): 70-74.

• 2014’江苏省人工智能学术会议 • 上一篇    下一篇

融合主题与语言模型的个性化标签推荐方法研究

李慧,马小平,胡 云,施 珺   

  1. 中国矿业大学信息与电气工程学院 徐州221116;淮海工学院计算机工程学院 连云港222005,中国矿业大学信息与电气工程学院 徐州221116,淮海工学院计算机工程学院 连云港222005;南京大学计算机科学与技术系 南京210093,淮海工学院计算机工程学院 连云港222005
  • 出版日期:2018-11-14 发布日期:2018-11-14
  • 基金资助:
    本文受国家自然科学基金(61403156,5),江苏省高校自然科学基金(14KJB520005),江苏省海洋资源开发研究院开放项目(JSIMR201323)资助

Personalized Tag Recommendation Algorithm Mixing Language Model and Topic

LI Hui, MA Xiao-ping, HU Yun and SHI Jun   

  • Online:2018-11-14 Published:2018-11-14

摘要: 随着Web的推广和普及,产生了越来越多的网络数据。 广泛应用了 标签系统 ,以便人们使用搜索技术来组织和使用这些信息。这些数据允许用户使用关键字(标签)注释资源,为传统的基于文本的信息检索提供了方案。为了支持用户选择正确的关键字,标签推荐算法应运而生。提出了一种个性化标签推荐方法,该方法综合了用户的资源标签与标签概率模型。该模型利用了简单语言模型和隐含狄利克雷分配模型,并针对现实世界的大型数据集进行了大量实验。实验表明,该个性化方法改进了标签推荐算法,推荐结果优于传统方法。

关键词: 标签,推荐,主题,潜在主题模型,个性化

Abstract: More and more content on the Web is generated by users.To organize this information and make it accessible via current search technology,tagging systems have gained tremendous popularity.We introduced an approach to personalized tag recommendation that combines a probabilistic model of tags from the resource with tags from the user.In this models,we investigated simple language models as well as Latent Dirichlet Allocation.Extensive experiments on a real world dataset crawled from a big tagging system show that personalization improves tag recommendation,and our approach significantly outperforms traditional approaches.

Key words: Tag,Recommendation,Topic,Latent topic models,Personality

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