An Effective Algorithm to Improve Recommender Systems using Evolutionary Computation Algorithms and Neural Network
Using Evolutionary Computation Algorithms and Neural Networks, an Effective Algorithm to Improve Recommender Systems
DOI:
https://doi.org/10.31185/wjcm.Vol1.Iss1.20Keywords:
Neural Network, Evolutionary Computation Algorithms, Recommender Systems, Selected:Effective AlgorithmAbstract
The growing Internet access and easy access to it have resulted in a significant increase in e-content, which, along with many benefits, has caused problems for users. Internet users simply cannot find the content they need from this massive amount of data. Users are faced with a lot of suggestions for choosing goods, buying items, selecting music and videos, and more. Advantage systems can be used to overcome these problems. Today, with the spread of people’s use of cyberspace, such as web sites and social networks, and increasing the need for conscious and clever selection of people, recommender systems has been extensively investigated. Although the neural network can identify the connections between the inputs and outputs of a dataset, but in order to achieve the proper performance of the neural network, a proper structure should be considered. We will use the mantle algorithm to determine this structure. The mantle algorithm is a form of traditional genetic algorithm that uses local search to reduce the time to achieve optimal response. Genetic algorithms are created to search across the search space, while the local search, the neighborhood of the neighborhood, finds every response found by the genetic algorithm to find better answers. This algorithm seeks to find the optimal values for the parameters of the neural network method, so optimal solutions of the memetic algorithm is considered to be used to set parameters for the neural network method. The results of this study show the desirable performance of the proposed approach in this study.
References
M. Athani, N. Pathak, A. U. Khan, and B. Gour, “Real Time Recommender System for Music Data,” International Journal of Computer Science and Network Security (IJCSNS), vol. 15, no. 8, pp. 88–88, 2019.
J. Bobadilla, F. Ortega, A. Hernando, and J. Alcalá, 2021.
S. Bostandjiev, J. O'donovan, and T. Höllerer, “TasteWeights: a visual interactive hybrid recommender system,” Proceedings of the sixth ACM conference on Recommender systems, pp. 2022–2022.
Carrer-Neto, M. Walter, R. L. Hernández-Alcaraz, F. Valencia-García, and García-Sánchez, “Social knowledge-based recommender system.
Application to the movies domain,” Expert Systems with applications, vol. 39, no. 12, pp. 10990–11000, 2012.
Zhou, J. Zhi-Hua, W. Wu, and Tang, “Ensembling neural networks: many could be better than all,” Artificial intelligence, vol. 137, no. 1-2, pp. 239–263, 2022.
Y. Koren and R. Bell, “Advances in collaborative filtering,” Recommender systems handbook, pp. 77–118, 2015.
N. Krasnogor and J. Smith, “A tutorial for competent memetic algorithms: model, taxonomy, and design issues,” IEEE Transactions on Evolutionary Computation, vol. 9, no. 5, pp. 474–488, 2015.
J. Lu, W. Dianshuang, M. Mingsong, W. Wei, and Z. Guangquan. “Recommender system application developments: a survey.” Decision Support Systems 74. 12-32. 2015
P. Kariryaa and A. Kumar, “Recommender Systems with Social Regularization,” 2019.
F. Ricci, “Context-aware music recommender systems: workshop keynote abstract,” Proceedings of the 21st International Conference on World Wide Web, pp. 865–866, 2012.
Y. Ong, L. Meng-Hiot, Z. Ning, and W. Kok-Wai. “Classification of adaptive memetic algorithms: a comparative study.” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 36, no. 1 (2016): 141-152.
L. Son and Hoang, “HU-FCF: A hybrid user-based fuzzy collaborative filtering method in Recommender Systems,” An International Journal, vol. 41, no. 15, pp. 6861–6870, 2014.
H. Yin, Y. Sun, B. Cui, Z. Hu, and L. Chen, “Lcars: a location-content-aware recommender system,” Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 221–229, 2021.
F. Gedikli and D. Jannach, “Rating items by rating tags,” Proceedings of the 2010 Workshop on Recommender Systems and the Social Web at ACM RecSys, pp. 25–32, 2020.
C. Christakou, S. Vrettos, and A. Stafylopatis, “A hybrid movie recommender system based on neural networks,” International Journal on Artificial Intelligence Tools, vol. 16, no. 05, pp. 771–92, 2017
Downloads
Published
Issue
Section
License
Copyright (c) 2022 Razieh Asgarnezhad, Safaa Saad Abdull Majeed , Zainab Aqeel Abbas, Sarah Sinan Salman
This work is licensed under a Creative Commons Attribution 4.0 International License.
