Multiobjective Personalized Recommendation Algorithm Using Extreme Point Guided Evolutionary Computation
Joint Authors
Lin, Qiuzhen
Wang, Xiaozhou
Hu, Bishan
Ma, Lijia
Chen, Fei
Coello Coello, Carlos A.
Jian-Qiang, Li
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-18, 18 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-11-12
Country of Publication
Egypt
No. of Pages
18
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Lin, Qiuzhen& Wang, Xiaozhou& Hu, Bishan& Ma, Lijia& Chen, Fei& Jian-Qiang, Li…[et al.]. 2018. Multiobjective Personalized Recommendation Algorithm Using Extreme Point Guided Evolutionary Computation. Complexity،Vol. 2018, no. 2018, pp.1-18.
https://search.emarefa.net/detail/BIM-1132974
Modern Language Association (MLA)
Lin, Qiuzhen…[et al.]. Multiobjective Personalized Recommendation Algorithm Using Extreme Point Guided Evolutionary Computation. Complexity No. 2018 (2018), pp.1-18.
https://search.emarefa.net/detail/BIM-1132974
American Medical Association (AMA)
Lin, Qiuzhen& Wang, Xiaozhou& Hu, Bishan& Ma, Lijia& Chen, Fei& Jian-Qiang, Li…[et al.]. Multiobjective Personalized Recommendation Algorithm Using Extreme Point Guided Evolutionary Computation. Complexity. 2018. Vol. 2018, no. 2018, pp.1-18.
https://search.emarefa.net/detail/BIM-1132974
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1132974