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March 21, 2026Open Access

NER: Nash Entropy-Regularised Scoring for Diversity-Aware Collaborative Filtering

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Key Points

The aim is to address popularity bias in item recommendations by promoting audience diversity.
Developed a Nash entropy-regularised scoring method.
Proved the mathematical properties of the new model, including the impact of diversity on recommendations.
Evaluated the model on the ML-100K dataset to determine performance improvements.
NER significantly improved recommendation accuracy compared to traditional methods.
Achieved a 5.6× increase in Precision on ML-100K.
Confirmed the importance of diversity in producing optimal item recommendations.

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Implication

Nash entropy regularised scoring enhances item recommendations, highlighting the importance of audience diversity in collaborative filtering.

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Cite This Study

Assil KHELIFI (2026) studied this question.

synapsesocial.com/papers/69c08b6ba48f6b84677f8833 https://doi.org/10.5281/zenodo.19154439

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