计算机工程

• 先进计算与数据处理 • 上一篇    下一篇

基于项目类别的协同过滤推荐算法多样性研究

叶锡君,龚玥   

  1. (南京农业大学信息科技学院,南京 210095)
  • 收稿日期:2014-09-17 出版日期:2015-10-15 发布日期:2015-10-15
  • 作者简介:叶锡君(1964-),男,副教授、博士,主研方向:数据挖掘,知识发现,生物信息学;龚玥,硕士研究生。
  • 基金项目:
    国家自然科学基金资助项目(61403205);江苏省高等教育教改研究基金资助项目(2013JSJG195)。

Study on Diversity of Collaborative Filtering Recommendation Algorithm Based on Item Category

YE Xijun,GONG Yue   

  1. (School of Information Science and Technology,Nanjing Agricultural University,Nanjing 210095,China)
  • Received:2014-09-17 Online:2015-10-15 Published:2015-10-15

摘要: 推荐系统的多样性正日益成为评价推荐质量的重要指标。为提高传统协同过滤推荐算法的个体多样性,在基于项目的协同过滤推荐算法的基础上,加入项目的类别属性信息,定义项目类别贡献函数以改进预测评分公式,提高与目标项目类别不完全相同的项目得分,实现最优项目推荐。实验结果表明,在保证一定推荐精确度的前提下,改进算法增强了推荐系统的个体多样性,具有更高的推荐质量。

关键词: 协同过滤, 多样性, 项目类别, 贡献函数, 预测评分, 列表内相似度指标

Abstract: Diversity of recommendation system becomes an important index of evaluating the quality of the recommendation.To improve the individual diversity of traditional collaborative filtering recommendation algorithm,the improved algorithm is based on item-based collaborative filtering recommendation algorithm,which adds item category information and defines a contribution function to optimize the formula of prediction score.It increases the items scores which have not exactly the same item category with the objective item,and achieves the best items recommendation.Experimental result proves the improved algorithm strengthens the individual diversity of recommendion system which at the same time keeps a high precision.As a result,it has a higher quality of recommendation.

Key words: collaborative filtering, diversity, item category, contribution function, prediction score, Intra-list Similarity(ILS) index

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