Browse > Article
http://dx.doi.org/10.3837/tiis.2021.02.004

A Many-objective Particle Swarm Optimization Algorithm Based on Multiple Criteria for Hybrid Recommendation System  

Hu, Zhaomin (Taiyuan University of Science and Technology, School of Computer Science and Technology)
Lan, Yang (Taiyuan University of Science and Technology, School of Computer Science and Technology)
Zhang, Zhixia (Taiyuan University of Science and Technology, School of Computer Science and Technology)
Cai, Xingjuan (Taiyuan University of Science and Technology, School of Computer Science and Technology)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.15, no.2, 2021 , pp. 442-460 More about this Journal
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.
Keywords
Many-objective Particle Swarm Optimization Algorithm; Recommendation System; Fitness Estimation Method; Internet of Things;
Citations & Related Records
연도 인용수 순위
  • Reference
1 A. Prajapati and S. Kumar, "PSO-MoSR: a PSO-based multi-objective software remodularisation," International Journal of Bio-Inspired Computation, vol. 15, no. 4, pp. 254-263, 2020.   DOI
2 Z. Cui, F. Xue, S. Zhang, X. Cai, Y. Cao, W. Zhang, and J. Chen, "A Hybrid BlockChain-Based Identity Authentication Scheme for Multi-WSN," IEEE Transactions on Services Computing, vol. 13, no.2, pp. 241-251, 2020.   DOI
3 P. Winoto and T. Y. Tang, "The role of user mood in movie recommendations," Expert Systems with Applications, vol. 37, no. 8, pp. 6086-6092, 2010.   DOI
4 S. K. Lee, Y. H. Cho, and S. H. Kim, "Collaborative filtering with ordinal scale-based implicit ratings for mobile music recommendations," Information Sciences, vol. 180, no. 11, pp. 2142-2155, 2010.   DOI
5 X. Cai, Z. Hu, and J. Chen, "A many-objective optimization recommendation algorithm based on knowledge mining," Information Sciences, vol. 537, pp. 148-161, 2020.   DOI
6 L. While, L. Bradstreet, and L. Barone, "A Fast Way of Calculating Exact Hypervolumes," IEEE Transactions on Evolutionary Computation, vol. 16, no. 1, pp. 86-95, 2012.   DOI
7 D. Zou, F. Wang, N. Yu, and X. Kong, "Solving many-objective optimisation problems by an improved particle swarm optimisation approach and a normalised penalty method," International Journal of Bio-Inspired Computation, vol. 14, no. 4, pp. 247-264, 2019.   DOI
8 A. J. Nebro, J. J. Durillo, J. Garcia-Nieto, C. A. Coello, F. Luna, and E. Alba, "SMPSO: A new PSO-based metaheuristic for multi-objective optimization," in Proc. of Symposium on Computational intelligence in Miulti-criteria Decision-Making(MCDM), pp. 66-73, 2009.
9 E. Zitzler and L. Thiele, "Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach," IEEE Transactions on Evolutionary Computation, vol. 3, no. 4, pp. 257-271, 1999.
10 E. Zitzler, L. Thiele, M. Laumanns, C. M. Fonseca, and V. G. D. Fonseca, "Performance assessment of multiobjective optimizers: an analysis and review," IEEE Transactions on Evolutionary Computation, vol. 7, no. 2, pp. 117-132, 2003.
11 J. Bobadilla, F. Ortega, A. Hernando, and A. Gutierrez, "Recommender systems survey," Knowledge-Based Systems, vol. 46, pp. 109-132, 2013.   DOI
12 X. Zhang, Y. Tian, and Y. Jin, "A Knee Point-Driven Evolutionary Algorithm for Many-Objective Optimization," IEEE Transactions on Evolutionary Computation, vol. 19, no. 6, pp. 761-776, 2015.   DOI
13 N. A. Moubayed, A. Petrovski, and J. Mccall, "D2MOPSO: MOPSO Based on Decomposition and Dominance with Archiving Using Crowding Distance in Objective and Solution Spaces," Evolutionary Computation, vol. 22, no. 1, pp. 47-77, 2014.   DOI
14 R. Cheng, Y. Jin, M. Olhofer, and B. Sendhoff, "A Reference Vector Guided Evolutionary Algorithm for Many-Objective Optimization," IEEE Transactions on Evolutionary Computation, vol. 20, no. 5, pp. 773-791, 2016.   DOI
15 W. Deng, J. Xu, Y. Song, and H. Zhao, "An effective improved co-evolution ant colony optimisation algorithm with multi-strategies and its application," International Journal of Bio-Inspired Computation, vol. 16, no. 3, pp. 158-170, 2020.   DOI
16 Y. Zhang, X. Cai, H. Zhu, and Y. Xu, "Application an improved swarming optimisation in attribute reduction," International Journal of Bio-Inspired Computation, vol. 16, no. 4, pp. 213-219, 2020.
17 L. Zhang, J. Xia, F. Cheng, J. Qiu, and X. Zhang, "Multi-Objective Optimization of Critical Node Detection Based on Cascade Model in Complex Networks," IEEE Transactions on Network Science and Engineering, vol. 7, no. 3, pp. 2052-2066, 2020.   DOI
18 X. Cai, S. Geng, J. Zhang, D. Wu, Z. Cui, W. Zhang, and J. Chen, "A Sharding Scheme based Many-objective Optimization Algorithm for Enhancing Security in Blockchain-enabled Industrial Internet of Things," IEEE Transactions on Industrial Informatics, 2021.
19 S. Yang, M. Li, X. Liu, and J. Zheng, "A Grid-Based Evolutionary Algorithm for Many-Objective Optimization," IEEE Transactions on Evolutionary Computation, vol. 17, no. 5, pp. 721-736, 2013.   DOI
20 T. Pamulapati, R. Mallipeddi, and P. N. Suganthan, "$I_{rm SDE}$ +-An Indicator for Multi and Many-Objective Optimization," IEEE Transactions on Evolutionary Computation, vol. 23, no. 2, pp. 346-352, 2019.   DOI
21 P. B. Throat, R. M. Goudar, and S. Barve, "Survey on Collaborative Filtering, Content-based Filtering and Hybrid Recommendation System," International Journal of Computer Applications, vol. 110, no. 4, pp. 31-36, 2015.   DOI
22 K. F. Yeung and Y. Yang, "A Proactive Personalized Mobile News Recommendation System," Developments in E-systems Engineering, pp. 207-212, 2010.
23 H. Azari and M. Moradipour, "Using kernel-based collocation methods to solve a delay partial differential equation with application to finance," International journal of computing science and mathematics, vol. 10, no. 1, pp. 105-114, 2019.   DOI
24 S. T. Cheng, G. J. Horng, and C. L. Chou, "The Adaptive Recommendation Mechanism for Distributed Group in Mobile Environments," IEEE Transactions on Systems Man and Cybernetics Part C, vol. 42, no. 6, pp. 1081-1092, 2012.   DOI
25 Y. Zuo, M. Gong, J. Zeng, L. Ma, and L. Jiao, "Personalized Recommendation Based on Evolutionary Multi-Objective Optimization [Research Frontier]," vol. 10, no. 1, pp. 52-62, 2015.   DOI
26 A. B. Barragans-Martinez, E. Costa-Montenegro, J. C. Burguillo, M. Rey-Lopez, F. A. Mikic-Fonte, and A. Peleteiro, "A hybrid content-based and item-based collaborative filtering approach to recommend TV programs enhanced with singular value decomposition," Information Sciences, vol. 180, no. 22, pp. 4290-4311, 2010.   DOI
27 C. Xu, A. S. Ding, and S. S. Liao, "A privacy-preserving recommendation method based on multi-objective optimisation for mobile users," International Journal of Bio-Inspired Computation, vol. 16, no. 1, pp. 23-32, 2020.   DOI
28 N. Hurley and M. Zhang, "Novelty and Diversity in Top-N Recommendation--Analysis and Evaluation," ACM Transactions on Internet Technology, vol. 10, no. 4, 2011.
29 B. Geng, L. Li, L. Jiao, M. Gong, Q. Cai, and Y. Wu, "NNIA-RS: A multi-objective optimization based recommender system," Physica A Statistical Mechanics and Its Applications, vol. 424, pp. 383-397, 2015.
30 B. M. Sarwar, G. Karypis, J. A. Konstan, and J. Riedl, "Item-based collaborative filtering recommendation algorithms," in Proc. of 10th International Conference on World Wide Web, pp. 285-295, 2001.
31 Z. Cui, X. Xu, F. Xue, X. Cai, Y. Cao, W. Zhang, and J. Chen, "Personalized Recommendation System Based on Collaborative Filtering for IoT Scenarios," IEEE Transactions on Services Computing, vol. 13, no. 4, pp. 685-695, 2020.   DOI
32 X. Cai, Z. Hu, P. Zhao, W. Zhang, and J. Chen, "A hybrid recommendation system with many-objective evolutionary algorithm," Expert Systems with Applications, vol. 159, 2020.
33 M. S. Mohamed and H. Duan, "Flight control system design using adaptive pigeon-inspired optimisation," International Journal of Bio-Inspired Computation, vol. 16, no. 3, pp. 133-147, 2020.   DOI
34 Q. Lin, S. Liu, Q. Zhu, C. Tang, R. Song, J. Chen, C. A. Coello, K. C. Wong, and J. Zhang, "Particle Swarm Optimization With a Balanceable Fitness Estimation for Many-Objective Optimization Problems," IEEE Transactions on Evolutionary Computation, vol. 22, no. 1, pp. 32-46, 2016.   DOI
35 Z. Cui, J. Zhang, D. Wu, X. Cai, H. Wang, W. Zhang, and J. Chen, "Hybrid many-objective particle swarm optimization algorithm for green coal production problem," Information Sciences, vol. 518, pp. 256-271, 2020.   DOI
36 X. Cai, S. Geng, D. Wu, J. Cai, and J. Chen, "A Multi-cloud Model based Many-objective Intelligent Algorithm for Efficient Task Scheduling in Internet of Things," IEEE Internet of Things Journal, 2020.
37 K. Deb and H. Jain, "An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints," IEEE Transactions on Evolutionary Computation, vol. 18, no. 4, pp. 577-601, 2014.   DOI
38 Z. Cui, Y. Zhao, Y. Cao, X. Cai, W. Zhang, and J. Chen, "Malicious code detection under 5G HetNets based on multi-objective RBM model," IEEE Network, 2020,