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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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2018/1716352 |
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| author | Qiuzhen Lin Xiaozhou Wang Bishan Hu Lijia Ma Fei Chen Jianqiang Li Carlos A. Coello Coello |
| author_facet | Qiuzhen Lin Xiaozhou Wang Bishan Hu Lijia Ma Fei Chen Jianqiang Li Carlos A. Coello Coello |
| author_sort | Qiuzhen Lin |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-8ac2a5cb96d64cecbb9a77c2f1523423 |
| institution | OA Journals |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-8ac2a5cb96d64cecbb9a77c2f15234232025-08-20T02:09:24ZengWileyComplexity1076-27871099-05262018-01-01201810.1155/2018/17163521716352Multiobjective Personalized Recommendation Algorithm Using Extreme Point Guided Evolutionary ComputationQiuzhen Lin0Xiaozhou Wang1Bishan Hu2Lijia Ma3Fei Chen4Jianqiang Li5Carlos A. Coello Coello6College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, ChinaCollege of Computer Science and Software Engineering, Shenzhen University, Shenzhen, ChinaCollege of Computer Science and Software Engineering, Shenzhen University, Shenzhen, ChinaCollege of Computer Science and Software Engineering, Shenzhen University, Shenzhen, ChinaCollege of Computer Science and Software Engineering, Shenzhen University, Shenzhen, ChinaCollege of Computer Science and Software Engineering, Shenzhen University, Shenzhen, ChinaCINVESTAV-IPN, Department of Computer Science, Mexico, DF, 07360, MexicoRecommender 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.http://dx.doi.org/10.1155/2018/1716352 |
| spellingShingle | Qiuzhen Lin Xiaozhou Wang Bishan Hu Lijia Ma Fei Chen Jianqiang Li Carlos A. Coello Coello Multiobjective Personalized Recommendation Algorithm Using Extreme Point Guided Evolutionary Computation Complexity |
| title | Multiobjective Personalized Recommendation Algorithm Using Extreme Point Guided Evolutionary Computation |
| title_full | Multiobjective Personalized Recommendation Algorithm Using Extreme Point Guided Evolutionary Computation |
| title_fullStr | Multiobjective Personalized Recommendation Algorithm Using Extreme Point Guided Evolutionary Computation |
| title_full_unstemmed | Multiobjective Personalized Recommendation Algorithm Using Extreme Point Guided Evolutionary Computation |
| title_short | Multiobjective Personalized Recommendation Algorithm Using Extreme Point Guided Evolutionary Computation |
| title_sort | multiobjective personalized recommendation algorithm using extreme point guided evolutionary computation |
| url | http://dx.doi.org/10.1155/2018/1716352 |
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