计算机科学 ›› 2014, Vol. 41 ›› Issue (Z11): 294-297.

• 数据挖掘 • 上一篇    下一篇

一种个性化推荐方法

朱宝,徐玲玉   

  1. 华南理工大学自动化科学与工程学院 广州510640;合肥工业大学管理学院 合肥231500
  • 出版日期:2018-11-14 发布日期:2018-11-14

Personalized Recommendation Technology

ZHU Bao and XU Ling-yu   

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

摘要: 提出了一种新的个性化推荐方法。该方法来源于对个性化推荐技术本质的研究。产出的方法包括一种用正态分布卷积性质所得到的离线相似度计算方法;一种通过计算物品与物品之间无差别的相似性操作次数得到离线相似度的方法;一种用类似于贝叶斯的方法来综合不同的相似度结果的方法。另外还提到一些用于工程实施的方法和技巧。所提方法已经在数据挖掘领域得到了成功的应用。

关键词: 个性化推荐,相似性,数据挖掘

Abstract: This paper proposed a new personalized recommendation method.The method is derived from the research on the essence of personalized recommendation technology.We arrived at a solution which includes several methods:one is calculating the offline similarity by the use of normal distribution’s convolution theories,one is calculating the offline similarity by calculating the number of similar operations between each pair of items,the last one is integrating the similarity results of different methods by the use of something similar to the Bayesian formula.We also mentioned some other methods and techniques used to engineering implementation.The method proposed in this paper has been successfully applied in the field of data mining.

Key words: Personalized recommendation,Similarity,Data mining

[1] Goldberg D,Nichols D,Oki B M,et al.Using collaborative filte-ring to weave an information apestry[J].Communications of the ACM,1992,35(12) :61-70
[2] Lee D D,Seung H S.Learning the Parts of Objects with Non-Negative Matrix Factorization[J].Nature,1999,401:788-791
[3] Zhou T,Ren J,et al.Bipartite network projection and personal recommendation[J].Physical Review E,2007,76(4):046115
[4] Zhou T,Kuscsik Z,Liu J G,et al.Solving the apparent diversity-accuracy dilemma of recommender systems[J].Proceedings of the National Academy of Sciences,2010,107(10):4511-4515
[5] 盛骤,谢式千,潘承毅然.概率论与数理统计(第四版)[M].北京:高等教育出版社,2008
[6] 范明,柴玉梅,昝红英.统计学习基础[M].北京:电子工业出版社,2004
[7] 米特拉.数字信号处理:基于计算机的方法(第四版)[M].北京:电子工业出版社,2012

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