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Multi-objective Immune Computing for Recommender Systems: A TOPSIS-Based Optimization Approach
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AI
DOI：10.1007/s12626-026-00215-y.png)
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Abstract
En 中文
Recommender systems enhance user experience through personalized suggestions, but traditional collaborative filtering methods often sacrifice diversity and novelty for accuracy, leading to user fatigue. Balancing these competing objectives is challenging, and existing multi-objective approaches generate overwhelming Pareto sets that complicate decision-making. We propose S-MOIA-T, a multi-objective recommendation framework that integrates Singular Value Decomposition (SVD) for rating prediction, Multi-Objective Immune Algorithm (MOIA) for Pareto-optimal solution generation, and TOPSIS for structured ranking and selection. This combination effectively balances accuracy, diversity, and novelty while simplifying recommendation selection. Extensive experiments on MovieLens-100K, Netflix, and Book-Crossing datasets demonstrate S-MOIA-T's superiority, achieving precision up to 0.83, diversity of 0.45, and novelty exceeding 1.2. These results significantly outperform baseline methods including User-CF, Item-CF, NMF, and state-of-the-art multi-objective algorithms (NNIA-RS, PMOEA, MOEA/D, MOPSO, EMO-GP). S-MOIA-T provides a practical, scalable solution for user-centric recommender systems.
Keywords:
Recommender systems
Multi-objective optimization
Evolutionary algorithms
Collaborative filtering
MOIA
TOPSIS
Journal
R
IF:
1
Papers:
28
Citations:
143