TIIS (한국인터넷정보학회)
Current Result Document : 69 / 70
| 한글제목(Korean Title) |
A Many-objective Particle Swarm Optimization Algorithm Based on Multiple Criteria for Hybrid Recommendation System |
| 영문제목(English Title) |
A Many-objective Particle Swarm Optimization Algorithm Based on Multiple Criteria for Hybrid Recommendation System |
| 저자(Author) |
Zhaomin Hu
Yang Lan
Zhixia Zhang
Xingjuan Cai
|
| 원문수록처(Citation) |
VOL 15 NO. 02 PP. 0442 ~ 0460 (2021. 02) |
한글내용
(Korean Abstract) |
|
영문내용
(English Abstract) |
Nowadays, recommendation systems (RSs) are applied to all aspects of online life. In order to overcome the problem that individuals who do not meet the constraints need to be regenerated when the many-objective evolutionary algorithm (MaOEA) solves the hybrid recommendation model, this paper proposes a many-objective particle swarm optimization algorithm based on multiple criteria (MaPSO-MC). A generation-based fitness evaluation strategy with diversity enhancement (GBFE-DE) and ISDE are coupled to comprehensively evaluate individual performance. At the same time, according to the characteristics of the model, the regional optimization has an impact on the individual update, and a many-objective evolutionary strategy based on bacterial foraging (MaBF) is used to improve the algorithm search speed. Experimental results prove that this algorithm has excellent convergence and diversity, and can produce accurate, diverse, novel and high coverage recommendations when solving recommendation models.
|
| 키워드(Keyword) |
Many-objective Particle Swarm Optimization-
-Algorithm
Recommendation System
Fitness Estimation Method
Internet of Things
|
| 파일첨부 |
PDF 다운로드
|
