A Hybrid Multi-Objective Immune Algorithm for Commercial Recommendation Systems - ZAIZI, Fatima Ezzahra; Qassimi, Sara; Rakrak, Said (Statistics, Optimization & Information Computing, 2026)
<audio class="audio-for-speech"></audio> 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.