A hybrid recommendation system with many-objective evolutionary algorithm

•Recommend the more and novel items based on accurate and diverse recommendations.•Mixing multiple recommendation technologies to improve recommendation performance.•The system is based on rating, which makes the recommendation more objective.•Clustering strategies are used to reduce recommended con...

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Bibliographic Details
Published in:Expert systems with applications Vol. 159; p. 113648
Main Authors: Cai, Xingjuan, Hu, Zhaoming, Zhao, Peng, Zhang, WenSheng, Chen, Jinjun
Format: Journal Article
Language:English
Published: New York Elsevier Ltd 30.11.2020
Elsevier BV
Subjects:
Collaborative filtering
Evolutionary algorithms
Genetic algorithms
Hybrid recommender algorithm
Hybrid systems
Many-objective optimization
Multiple objective analysis
Optimization
Recommendation systems
Recommender systems
User requirements
Recommendation systems
Hybrid recommender algorithm
Many-objective optimization
Collaborative filtering
ISSN:0957-4174, 1873-6793
Online Access:Get full text
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Summary:•Recommend the more and novel items based on accurate and diverse recommendations.•Mixing multiple recommendation technologies to improve recommendation performance.•The system is based on rating, which makes the recommendation more objective.•Clustering strategies are used to reduce recommended consumption. Recommendation system (RS) is a technology that provides accurate recommendations to users. However, it is not comprehensive to only consider the accuracy of the recommendation because users have different requirements. To improve the comprehensive performance, this paper presents a hybrid recommendation model based on many-objective optimization, which can simultaneously optimize the accuracy, diversity, novelty and coverage of recommendation. This model enhances the robustness of recommendations by mixing three different basic recommendation technologies. Additionally, we solve it with many-objective evolutionary algorithm (MaOEA) and test it extensively. Experimental results demonstrate the effectiveness of the presented model, which can provide the recommendations with more and novel items on the basis of accurate and diverse.
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ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2020.113648