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A hybrid recommendation system with many-objective evolutionary algorithm
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DOI：10.1016/j.eswa.2020.113648.png)
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Abstract
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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. (C) 2020 Elsevier Ltd. All rights reserved.
Keywords:
Recommendation systems
Many-objective optimization
Hybrid recommender algorithm
Collaborative filtering
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