Neural Network Feature Selection Based on Collaborative Filtering Recommender Systems for User Classification

Elham Abdulwahab Anaam ^1,*, Su-Cheng Haw ^1,*, Kok-Why Ng¹, Palanichamy Naveen¹

¹ Faculty of Computing and Informatics, Multimedia University, 63100, Cyberjaya, Malaysia

 Emails: anaamelham@gmail.com; sucheng@mmu.edu.my;  kwng@mmu.edu.my; 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