2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 15 , Issue 2 , PP: 155-164, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Elham Abdulwahab Anaam 1 * , Su-Cheng Haw 2 , Kok-Why Ng 3 , Palanichamy Naveen 4
Doi: https://doi.org/10.54216/FPA.150214
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.
Recommendation system , Neural Network , Users Classifications , Collaborative Filtering , Personalization
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