A Hybrid Multi-Objective Immune Algorithm for Commercial Recommendation Systems

Authors

  • Fatima Ezzahra ZAIZI L2IS, Computer Science, FSTG, Cadi Ayyad University
  • Sara Qassimi L2IS, Computer Science, FSTG, Cadi Ayyad University
  • Said Rakrak L2IS, Computer Science, FSTG, Cadi Ayyad University

DOI:

https://doi.org/10.19139/soic-2310-5070-2768

Keywords:

Recommender systems, Multi-objective optimization, Multi-Objective Evolutionary algorithms, MOIA Algorithm, Commercial, Collaborative filtering

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.

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Published

2026-01-12

How to Cite

ZAIZI, F. E., Qassimi, S. ., & Rakrak, S. (2026). A Hybrid Multi-Objective Immune Algorithm for Commercial Recommendation Systems. Statistics, Optimization & Information Computing, 15(2), 1433–1444. https://doi.org/10.19139/soic-2310-5070-2768

Issue

Vol. 15 No. 2 (2026): ICCSAI'24

Section

Research Articles

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