A new uncertainty-aware similarity for user-based collaborative filtering

Khadidja Belmessous; Faouzi Sebbak; M'hamed Mataoui; Walid Cherifi

Kybernetika (2024)

  • Volume: 60, Issue: 4, page 446-474
  • ISSN: 0023-5954

Abstract

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User-based Collaborative Filtering (UBCF) is a common approach in Recommender Systems (RS). Essentially, UBCF predicts unprovided entries for the target user by selecting similar neighbors. The effectiveness of UBCF greatly depends on the selected similarity measure and the subsequent choice of neighbors. This paper presents a new Uncertainty-Aware Similarity measure "UASim" which enhances CF by accurately calculating how similar, dissimilar, and uncertain users' preferences are. Uncertainty is a key factor of "UASim" that is managed in the neighborhood selection step of CF. Extensive experimental evaluation, conducted on Flixter, Movielens-100K, and Movielens-1M datasets, indicates that "UASim" shows better performance compared to many representative predefined similarity measures. The proposed measure demonstrates enhancements across various performance indicators, namely: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), coverage, and the F-score.

How to cite

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Belmessous, Khadidja, et al. "A new uncertainty-aware similarity for user-based collaborative filtering." Kybernetika 60.4 (2024): 446-474. <http://eudml.org/doc/299405>.

@article{Belmessous2024,
abstract = {User-based Collaborative Filtering (UBCF) is a common approach in Recommender Systems (RS). Essentially, UBCF predicts unprovided entries for the target user by selecting similar neighbors. The effectiveness of UBCF greatly depends on the selected similarity measure and the subsequent choice of neighbors. This paper presents a new Uncertainty-Aware Similarity measure "UASim" which enhances CF by accurately calculating how similar, dissimilar, and uncertain users' preferences are. Uncertainty is a key factor of "UASim" that is managed in the neighborhood selection step of CF. Extensive experimental evaluation, conducted on Flixter, Movielens-100K, and Movielens-1M datasets, indicates that "UASim" shows better performance compared to many representative predefined similarity measures. The proposed measure demonstrates enhancements across various performance indicators, namely: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), coverage, and the F-score.},
author = {Belmessous, Khadidja, Sebbak, Faouzi, Mataoui, M'hamed, Cherifi, Walid},
journal = {Kybernetika},
keywords = {collaborative filtering; similarity; subjective logic; uncertainty},
language = {eng},
number = {4},
pages = {446-474},
publisher = {Institute of Information Theory and Automation AS CR},
title = {A new uncertainty-aware similarity for user-based collaborative filtering},
url = {http://eudml.org/doc/299405},
volume = {60},
year = {2024},
}

TY - JOUR
AU - Belmessous, Khadidja
AU - Sebbak, Faouzi
AU - Mataoui, M'hamed
AU - Cherifi, Walid
TI - A new uncertainty-aware similarity for user-based collaborative filtering
JO - Kybernetika
PY - 2024
PB - Institute of Information Theory and Automation AS CR
VL - 60
IS - 4
SP - 446
EP - 474
AB - User-based Collaborative Filtering (UBCF) is a common approach in Recommender Systems (RS). Essentially, UBCF predicts unprovided entries for the target user by selecting similar neighbors. The effectiveness of UBCF greatly depends on the selected similarity measure and the subsequent choice of neighbors. This paper presents a new Uncertainty-Aware Similarity measure "UASim" which enhances CF by accurately calculating how similar, dissimilar, and uncertain users' preferences are. Uncertainty is a key factor of "UASim" that is managed in the neighborhood selection step of CF. Extensive experimental evaluation, conducted on Flixter, Movielens-100K, and Movielens-1M datasets, indicates that "UASim" shows better performance compared to many representative predefined similarity measures. The proposed measure demonstrates enhancements across various performance indicators, namely: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), coverage, and the F-score.
LA - eng
KW - collaborative filtering; similarity; subjective logic; uncertainty
UR - http://eudml.org/doc/299405
ER -

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