Adaptive Recommendation Model Based on User Behaviors

doi:10.11925/infotech.2096-3467.2020.0349

Data Analysis and Knowledge Discovery

2021, Vol. 5

Issue (4): 103-114 DOI: 10.11925/infotech.2096-3467.2020.0349

Current Issue | Archive | Adv Search

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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Abstract

[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 words： Three-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: xzyhytxbw@163.com

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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.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2020.0349 OR https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2021/V5/I4/103

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)

Model Performance Under Different Indicators

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