What question did this study set out to answer?

This research aims to enhance recommendation accuracy for gray sheep users by integrating neutrosophic clustering with item-based collaborative filtering.

March 23, 2026Scientific ReportsOpen Access

A neutrosophic clustering approach to handle recommendation uncertainty for gray sheep users

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Authors

DSDina Samir EREman Abd El Reheem SDSaad M. Darwish

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Overview

Experimental evaluation shows improved recommendations for gray sheep users, indicating enhanced system performance.

Key Points

This research aims to enhance recommendation accuracy for gray sheep users by integrating neutrosophic clustering with item-based collaborative filtering.
Introduced neutrosophic k-means clustering to address user preference ambiguity.
Applied item-based collaborative filtering for generating recommendations.
Conducted experiments using the MovieLens 100 K dataset to assess model performance.
Achieved precision of 88.70%, recall of 90.90%, and F1-score of 89.79%.
Reduced error rates with MAE of 0.534 and RMSE of 0.719.
Demonstrated generalizability of neutrosophic k-means in different datasets like Book-Crossing.

Cite This Study

Samir et al. (2026) studied this question.

synapsesocial.com/papers/69c0e029fddb9876e79c1bc8 https://doi.org/10.1038/s41598-026-41651-8

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March 23, 2026Scientific ReportsOpen Access

A neutrosophic clustering approach to handle recommendation uncertainty for gray sheep users

View Full Paper

Ask AI

Bookmark

Authors

DSDina Samir EREman Abd El Reheem SDSaad M. Darwish

Discussion

Member takes

Overview

Experimental evaluation shows improved recommendations for gray sheep users, indicating enhanced system performance.

Key Points

This research aims to enhance recommendation accuracy for gray sheep users by integrating neutrosophic clustering with item-based collaborative filtering.
Introduced neutrosophic k-means clustering to address user preference ambiguity.
Applied item-based collaborative filtering for generating recommendations.
Conducted experiments using the MovieLens 100 K dataset to assess model performance.
Achieved precision of 88.70%, recall of 90.90%, and F1-score of 89.79%.
Reduced error rates with MAE of 0.534 and RMSE of 0.719.
Demonstrated generalizability of neutrosophic k-means in different datasets like Book-Crossing.

Cite This Study

Samir et al. (2026) studied this question.

synapsesocial.com/papers/69c0e029fddb9876e79c1bc8 https://doi.org/10.1038/s41598-026-41651-8

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