Research on Personalized Recommender System Adapting to Different Business

Caixia Tao; Hai Yuan; Kang Chen; Anhua Ma

doi:10.3969/j.issn.1000-0801.2014.08.019

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Research on Personalized Recommender System Adapting to Different Business

Operating technology wide-angle | 更新时间：2024-06-05

- Research on Personalized Recommender System Adapting to Different Business
- Telecommunications Science Vol. 30, Issue 8, Pages: 131-135(2014)
- 作者机构：
  
  1. 中国电信股份有限公司广东研究院广州510630
  2. 中国电信股份有限公司江苏分公司南京210037
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.1000-0801.2014.08.019
  CLC：
- Published Online：2014-08，
  
  Published：15 August 2014
- 稿件说明：
移动端阅览
Caixia Tao, Hai Yuan, Kang Chen, et al. Research on Personalized Recommender System Adapting to Different Business[J]. Telecommunications science, 2014, 30(8): 131-135.
DOI：

Caixia Tao, Hai Yuan, Kang Chen, et al. Research on Personalized Recommender System Adapting to Different Business[J]. Telecommunications science, 2014, 30(8): 131-135. DOI： 10.3969/j.issn.1000-0801.2014.08.019.

摘要

互联网技术的迅速发展，尤其是以个性化为主要特点的Web 2.0的不断成熟，使得大量信息同时呈现在人们面前，个性化推荐技术的价值日益凸显。提出了一种灵活适应不同业务的个性化推荐系统设计方案，采集并分析用户的所有显式行为和隐式行为，结合用户行为配置文件和熵值法分析用户对物品的兴趣度，并引入时间遗忘函数，解决用户兴趣漂移问题，然后基于协同过滤技术得到用户的个性化推荐列表。通过实际数据进行测试，给出了系统推荐效果评估分析。

Abstract

The rapid development of internet technology

especially the developed of Web 2.0 which has the main feature of personality

making a lot of information in front of people at the same time. The value of personalized recommendation technology has become increasingly prominent. A design of personalized recommender system adapting to different business was presented

including the collection and analysis of user's all explicit and implicit behavior

using user behavior profiles and entropy method to determine the weight of the behavior for analyzing user interest

the introduction of time forgetting function to solve the problem of user interest drift

and getting a list of the user's personalized recommendation based on collaborative filtering technology. Finally

the system test assessment analysis based on the actual data was provided.

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Keywords

references

王国霞 , 刘贺平 . 个性化推荐系统综述 . 计算机工程与应用 , 2012 , 48 ( 7 ): 66 ～ 76

Koychev I , Schwab I . Adaptation to drifting user's interests . Proceedings of ECML2000/MLnet Workshop on Machine Learning in the New Information Age ， Barcelona, Catalonia, Spain ， 2000

李宁 , 王子磊 , 吴刚等 . 个性化影片推荐系统中用户模型研究 . 计算机应用与软件 , 2010 , 27 ( 12 ): 51 ～ 54

马宏伟 , 张光卫 , 李鹏 . 协同过滤推荐算法综述 . 小型微型计算机系统 , 2009 , 30 ( 7 ): 1282 ～ 1288

丁振国 , 陈静 . 基于关联规则的个性化推荐系统 . 计算机集成制造系统 , 2003 , 9 ( 10 ): 891 ～ 893

吴兵 , 叶春明 . 基于效用的个性化推荐方法 . 计算机工程 , 2012 , 38 ( 4 ): 49 ～ 51

刘平峰 , 聂规划 , 陈冬林 . 基于知识的电子商务智能推荐系统平台设计 . 计算机工程与应用 , 2007 , 43 ( 19 ): 199 ～ 201

Goldberg D , Nichols , Oki B M , et al . Using collaborative filtering to weave an information tapestry . Communications of the ACM , 1992 , 35 ( 12 ): 61 ～ 70

林霜梅 , 汪更生 , 陈弃秋 . 个性化推荐系统中的用户建模及特征选择 . 计算机工程 , 2007 ( 9 )

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