计算机科学 ›› 2019, Vol. 46 ›› Issue (2): 210-214.doi: 10.11896/j.issn.1002-137X.2019.02.032

• 人工智能 • 上一篇    下一篇

融合Jensen-Shannon散度的推荐算法

王永1, 王永东1, 邓江洲1, 张璞2   

  1. 重庆邮电大学经济管理学院 重庆4000651
    重庆邮电大学计算机科学与技术学院 重庆4000652
  • 收稿日期:2017-12-06 出版日期:2019-02-25 发布日期:2019-02-25
  • 通讯作者: 王 永(1977-),男,博士,教授,CCF会员,主要研究方向为数据挖掘、信息系统和加密算法等,E-mail:wangyong1@cqupt.edu.cn
  • 作者简介:王永东(1994-),男,硕士生,主要研究方向为推荐算法;邓江洲(1993-),男,硕士生,主要研究方向为数据挖掘和文本处理;张 璞(1976-),男,博士,副教授,主要研究方向为自然语言和数据挖掘等。
  • 基金资助:
    本文受国家社会科学基金项目(15XGL024),重庆市前沿与应用基础研究计划项目(cstc2015jcyjA40025)资助。

Recommendation Algorithm Based on Jensen-Shannon Divergence

WANG Yong1, WANG Yong-dong1, DENG Jiang-zhou1, ZHANG Pu2   

  1. School of Economics and Managements,Chongqing University of Posts and Telecommunications,Chongqing 400065,China1
    School of Computer Science and Technology,Chongqing University of Posts and Telecommunications,Chongqing 400065,China2
  • Received:2017-12-06 Online:2019-02-25 Published:2019-02-25

摘要: 为充分利用所有评分,缓解数据稀疏性问题,将概率统计领域的Jensen-Shannon(JS)散度引入相似性度量中,提出了一种新的项目相似性度量算法。该算法将项目的评分信息转化为评分值密度,并依据评分值的密度分布来计算项目相似性。同时,引入评分数量因子,进一步提升了基于JS的相似性度量方法的性能。最后,以基于JS的相似性度量方法为基础,设计了相应的协同过滤算法。在MovieLens数据集上的实验结果表明,所提算法在预测误差和推荐准确性方面均有良好的表现。因此,该算法在推荐系统中具有很好的应用潜力。

关键词: Jensen-Shannon散度, 评分值密度, 相似性度量, 协同过滤, 数据稀疏性

Abstract: To fully utilize all the ratings and weaken the problem of data sparsity,the Jensen-Shannon divergence in statistics field was used to design a new similarity measure for items.In this similarity measure,the ratings for items are converted to the density of rating values.Then,the item similarity is calculated according to the density of rating values.Meanwhile,the factor for the number of ratings is also considered to further enhance the performance of the proposed similarity measure based on JS divergence.Finally,a collaborative filtering recommendation algorithm is presented according to the JS-divergence-based item similarity.The test results on MovieLens dataset show that the proposed algorithm has good performance in prediction error and recommendation precision.Therefore,it has high potential to be applied in recommendation system.

Key words: Jensen-Shannon divergence, Density of ratings, Similarity measure, Collaborative filtering, Data sparsity

中图分类号: 

  • TP391
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