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
[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.
向卓元,刘志聪,吴玉. 基于用户行为自适应推荐模型研究 *[J]. 数据分析与知识发现, 2021, 5(4): 103-114.
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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