Citation

Anaam, Elham Abdulwahab and Haw, Su Cheng and Ng, Kok Why and Naveen, Palanichamy (2024) Neural Network Feature Selection Based on Collaborative Filtering Recommender Systems for User Classification. Fusion: Practice and Applications, 15 (2). pp. 155-164. ISSN 2770-0070

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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.

Item Type:	Article
Uncontrolled Keywords:	Neural Network
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	03 Jul 2024 02:23
Last Modified:	03 Jul 2024 02:23
URII:	http://shdl.mmu.edu.my/id/eprint/12573

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