1
Faculty of Computing and Informatics, Multimedia University, 63100, Cyberjaya, Malaysia
(anaamelham@gmail.com)
2
Faculty of Computing and Informatics, Multimedia University, 63100, Cyberjaya, Malaysia
(sucheng@mmu.edu.my)
3
Faculty of Computing and Informatics, Multimedia University, 63100, Cyberjaya, Malaysia
(kwng@mmu.edu.my)
4
Faculty of Computing and Informatics, Multimedia University, 63100, Cyberjaya, Malaysia
(p.naveen@mmu.edu.my)
Abstract :
In today’s competitive markets, it is crucial to render personalized assistance tailored to unique individual’s needs. To accomplish this goal, a recommender system represents a noteworthy progression in collaborative filtering recommender systems. This shift highlights a broader research focus that extends beyond algorithms to encompass a diverse array of questions related to the functionality of the recommender. The identification accuracy must be assessed as a function of how well the suggested approach fits with a user's wants and needs, particularly in the context of collaborative constraint-based functions. The next phase of research must focus on defining parameters for assessment which may be used to compare the performance of constraint-based algorithms across a wide variety of diverse issues. It is currently necessary to design, or at criteria for assessment for constraint-based algorithms. We have addressed key research challenges related to the following topics: constraint-aware machine learning, understanding parameters in solution spaces, metrics for assessing constraint-based systems, algorithm selection, machine learning considerations, and investigating constraint-based platforms, and elucidations.
Keywords :
Recommendation system; Neural Network; Users Classifications; Collaborative Filtering; Personalization
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| Style | # |
|---|---|
| MLA | Elham Abdulwahab Anaam , Su-Cheng Haw , Kok-Why Ng, Palanichamy Naveen. "Neural Network Feature Selection Based on Collaborative Filtering Recommender Systems for User Classification." Fusion: Practice and Applications, Vol. 15, No. 2, 2024 ,PP. 155-164 (Doi : https://doi.org/10.54216/FPA.150214) |
| APA | Elham Abdulwahab Anaam , Su-Cheng Haw , Kok-Why Ng, Palanichamy Naveen. (2024). Neural Network Feature Selection Based on Collaborative Filtering Recommender Systems for User Classification. Journal of Fusion: Practice and Applications, 15 ( 2 ), 155-164 (Doi : https://doi.org/10.54216/FPA.150214) |
| Chicago | Elham Abdulwahab Anaam , Su-Cheng Haw , Kok-Why Ng, Palanichamy Naveen. "Neural Network Feature Selection Based on Collaborative Filtering Recommender Systems for User Classification." Journal of Fusion: Practice and Applications, 15 no. 2 (2024): 155-164 (Doi : https://doi.org/10.54216/FPA.150214) |
| Harvard | Elham Abdulwahab Anaam , Su-Cheng Haw , Kok-Why Ng, Palanichamy Naveen. (2024). Neural Network Feature Selection Based on Collaborative Filtering Recommender Systems for User Classification. Journal of Fusion: Practice and Applications, 15 ( 2 ), 155-164 (Doi : https://doi.org/10.54216/FPA.150214) |
| Vancouver | Elham Abdulwahab Anaam , Su-Cheng Haw , Kok-Why Ng, Palanichamy Naveen. Neural Network Feature Selection Based on Collaborative Filtering Recommender Systems for User Classification. Journal of Fusion: Practice and Applications, (2024); 15 ( 2 ): 155-164 (Doi : https://doi.org/10.54216/FPA.150214) |
| IEEE | Elham Abdulwahab Anaam, Su-Cheng Haw, Kok-Why Ng, Palanichamy Naveen, Neural Network Feature Selection Based on Collaborative Filtering Recommender Systems for User Classification, Journal of Fusion: Practice and Applications, Vol. 15 , No. 2 , (2024) : 155-164 (Doi : https://doi.org/10.54216/FPA.150214) |