Fusion: Practice and Applications FPA 2692-4048 2770-0070 10.54216/FPA https://www.americaspg.com/journals/show/2693 2018 2018 Faculty of Computing and Informatics, Multimedia University, 63100, Cyberjaya, Malaysia Elham Elham Faculty of Computing and Informatics, Multimedia University, 63100, Cyberjaya, Malaysia Su-Cheng .. Faculty of Computing and Informatics, Multimedia University, 63100, Cyberjaya, Malaysia Kok-Why Ng Faculty of Computing and Informatics, Multimedia University, 63100, Cyberjaya, Malaysia Palanichamy Naveen 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.  2024 2024 155 164 10.54216/FPA.150214 https://www.americaspg.com/articleinfo/3/show/2693