Volume 15 • Issue 2 • PP: 155-164 • 2024
Neural Network Feature Selection Based on Collaborative Filtering Recommender Systems for User Classification
Open Access & Copyright
© 2024 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
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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.
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