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...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Expert systems with applications Ročník 159; s. 113648
Hlavní autoři: Cai, Xingjuan, Hu, Zhaoming, Zhao, Peng, Zhang, WenSheng, Chen, Jinjun
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Elsevier Ltd 30.11.2020
Elsevier BV
Témata:
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
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:•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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2020.113648