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Data Analysis and Knowledge Discovery  2021, Vol. 5 Issue (4): 103-114    DOI: 10.11925/infotech.2096-3467.2020.0349
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Adaptive Recommendation Model Based on User Behaviors
Xiang Zhuoyuan1(),Liu Zhicong2,Wu Yu1
1School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan 430073, China
2Data Center of China Construction Bank, Wuhan 430073, China
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[Objective] This paper proposes an adaptive recommendation model based on user’s behaviors, aiming to address the issues of one model only working for one user type. [Methods] We standardized the recommendation process with a three-tier collaborative structure. The first layer classified users to create different recommendation channels. The second layer matched the improved recommendation sub-algorithm with corresponding channels. The third layer introduced feature weighting to form a recommendation pool, from which the items were selected and recommended to users. [Results] The accuracy, recall, coverage and popularity of the proposed model were 0.24, 0.17, 0.50 and 4.40, which were better than the mainstream models. [Limitations] Our recommendation algorithm cannot work on datasets without scores. [Conclusions] The proposed model can learn the preferences of users and make better recommendations.

Key wordsThree-Layer Collaboration      Adaptive Recommendation      Similarity Mixing      Machine Learning     
Received: 22 April 2020      Published: 17 May 2021
ZTFLH:  G353  
Fund:National Natural Science Foundation of China(61702553)
Corresponding Authors: Xiang Zhuoyuan     E-mail:

Cite this article:

Xiang Zhuoyuan,Liu Zhicong,Wu Yu. Adaptive Recommendation Model Based on User Behaviors. Data Analysis and Knowledge Discovery, 2021, 5(4): 103-114.

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Three-Tier Collaboration Framework
Three Tables Form a Star Model
Importance Coefficient of User Characteristics
α,β Parameter Settings
α,β Parameter Settings
Comparison of the Models (Accuracy)
Comparison of the Models (Recall)
Comparison of the Models (Coverage)
Comparison of the Models (Popularity)
指标 本文 UserCF ItemCF CB Mix
准确率 0.24 0.09 0.08 0.04 0.08
召回率 0.17 0.17 0.16 0.08 0.16
覆盖率 0.50 0.42 0.41 0.59 0.49
流行度 4.40 4.93 4.91 3.56 4.86
Model Performance Under Different Indicators
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