Multiobjective Personalized Recommendation Algorithm Using Extreme Point Guided Evolutionary Computation

Recommender systems suggest items to users based on their potential interests, and they are important to alleviate the search and selection pressures induced by the increasing item information. Classical recommender systems mainly focus on the accuracy of recommendation. However, with the increase o...

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Bibliographic Details
Published in:Complexity (New York, N.Y.) N.Y.), 2018-01, Vol.2018 (2018), p.1-18
Main Authors: Coello Coello, Carlos A., Chen, Fei, Ma, Lijia, Hu, Bishan, Wang, Xiaozhou, Lin, Qiuzhen, Jian-Qiang, Li
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Collaboration
Crossovers
Cybernetics
Design
Evolutionary algorithms
Evolutionary computation
Genetic algorithms
Knowledge
Multiple objective analysis
Objectives
Recommender systems
State of the art
User satisfaction
Web 2.0
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Summary:Recommender systems suggest items to users based on their potential interests, and they are important to alleviate the search and selection pressures induced by the increasing item information. Classical recommender systems mainly focus on the accuracy of recommendation. However, with the increase of the diversified demands of users, multiple metrics which may conflict with each other have to be considered in modern recommender systems, especially for the personalized recommender system. In this paper, we design a personalized recommendation system considering the three conflicting objectives, i.e., the accuracy, diversity, and novelty. Then, to let the system provide more comprehensive recommended items, we present a multiobjective personalized recommendation algorithm using extreme point guided evolutionary computation (called MOEA-EPG). The proposed MOEA-EPG is guided by three extreme points and its crossover operator is designed for better satisfying the demands of users. The experimental results validate the effectiveness of MOEA-EPG when compared to some state-of-the-art recommendation algorithms in terms of accuracy, diversity, and novelty on recommendation.
ISSN:1076-2787
1099-0526
DOI:10.1155/2018/1716352