Year: 2026
Type: article
Abstract: Recommender systems are essential components of modern commercial platforms, significantly impacting user engagement and sales. However, balancing accuracy, diversity, and novelty in recommendations remains a challenge. This study presents a novel approach, SVD-MOIA, which combines Singular Value Decomposition (SVD) with the Multi-Objective Immune Algorithm (MOIA) to address this issue. The proposed method aims to enhance recommendation accuracy while simultaneously increasing diversity and novelty. Evaluated on Amazon Product Review, Book-Crossing, and IMDb movies datasets, SVD-MOIA demonstrates superior performance over traditional algorithms. The results show that SVD-MOIA effectively balances multiple conflicting objectives, providing valuable insights for improving user satisfaction and engagement in commercial recommender systems.
Institution: Cadi Ayyad University
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7124138168
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