A Proposed λ_Mining Model for Hierarchical Multi-Level Predictive Recommendations

Abstract: Delivering the most suitable products and services essentially relies on successfully exploring the potential relationship between customers and products. This immense need for intelligent exploration has led to the emergence of recommendation systems. In an environment where an immense variety exists, it is vital for buyers to own an intelligent exploratory map to guide them in finding their choices. Personalization has proven to be a successful contributor to recommenders. It provides an accurate guide to explore the users’ preferences. In the field of recommendation systems, the performance of the systems has been continuously measured by their success in accurate, personalized recommendations. There is no argument that personalization is one key success; however, this research argues that recommendation systems are not only about personalization. Other success factors should be considered in targeting optimality. The current research explores the hierarchy map representing the strengths and dependencies of the recommendation systems pillars associated with their influence level and relationships. Moreover, the research proposes a novel predictive approach that applies a hybrid of content and collaborative filtering recommendation systems to provide the most suitable customer recommendations effectively. The model utilizes a proposed features selection approach to detect the most significant features and explore the most effective associations’ schemes for the recommendations label feature. The proposed model is validated using a benchmark dataset by extracting direct and transitive associations and following the identified schematic for the required recommendations. The classification techniques are applied, proving the model's applicability with an accuracy ranging from 96% to 99%.

Yousef S. Alsahafi, Ayman E. Khedr and Amira M. Idrees. “A Proposed λ_Mining Model for Hierarchical Multi-Level Predictive Recommendations”. International Journal of Advanced Computer Science and Applications (IJACSA) 15.9 (2024). http://dx.doi.org/10.14569/IJACSA.2024.0150997

@article{Alsahafi2024,
title = {A Proposed λ_Mining Model for Hierarchical Multi-Level Predictive Recommendations},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150997},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150997},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {9},
author = {Yousef S. Alsahafi and Ayman E. Khedr and Amira M. Idrees}
}