Personalized recommendation algorithm based on user behavior analysis

Jun JIA; Bin ZHANG; Zhiyuan LI; Wei WEI; Hao WEI

doi:10.11959/j.issn.2096-6652.201947

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Personalized recommendation algorithm based on user behavior analysis

Regular Papers | 更新时间：2024-06-05

- Personalized recommendation algorithm based on user behavior analysis
- Chinese Journal of Intelligent Science and Technology Vol. 1, Issue 4, Pages: 421-426(2019)
- 作者机构：
  
  1. 青岛海尔（胶州）空调器有限公司，山东青岛 266000
  2. 山东科技大学计算机科学与工程学院，山东青岛 266590
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-6652.201947
  CLC： TP391.3
- Published Online：2019-12，
  
  Published：20 December 2019
- 稿件说明：
移动端阅览
Jun JIA, Bin ZHANG, Zhiyuan LI, et al. Personalized recommendation algorithm based on user behavior analysis[J]. Chinese journal of intelligent science and technology, 2019, 1(4): 421-426.
DOI：

Jun JIA, Bin ZHANG, Zhiyuan LI, et al. Personalized recommendation algorithm based on user behavior analysis[J]. Chinese journal of intelligent science and technology, 2019, 1(4): 421-426. DOI： 10.11959/j.issn.2096-6652.201947.

摘要

随着商业智能系统和数据挖掘技术的发展，用户的行为数据对企业决策产生了重要的影响。网络电子商务平台可以利用这些数据分析后的结果，对特定的用户推送他们感兴趣的商品，这样能增强用户黏度，提高平台的商业价值。提出一种基于用户行为分析的个性化推荐算法，将用户的行为信息转化为用户评分矩阵，且提出一种改进的正则化非负矩阵分解算法，在原始正则化非负矩阵分解算法的基础上加入偏置信息。改进算法充分挖掘用户在网页上点击、购买、浏览、收藏等行为信息，将用户感兴趣的商品及时推送给用户。实验结果验证了本文所提出的两种算法的有效性和高效性。

Abstract

With the development of business intelligence system and data mining technology

user behavior data has an important impact on enterprise decision-making.For the network e-commerce platform

the results of these data analysis can be used to push items of interest to specific users

which can enhance the user experience and the business value of the platform.A personalized recommendation algorithm based on user behavior analysis was proposed

which transforms user behavior information into user rating matrix

and an improved regularized nonnegative matrix decomposition algorithm was also proposed

which adds bias information to the original regularized nonnegative matrix decomposition.This algorithm can fully mine the user behavior information such as click

purchase

browse

collect

etc.

and actively push the items of interest to the users.The experimental results verify the effectiveness and efficiency of the algorithm.

关键词

Keywords

references

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