Document (#41461)

Author

Zhu, J.
Han, L.
Gou, Z.
Yuan, X.

Title

¬A fuzzy clustering-based denoising model for evaluating uncertainty in collaborative filtering recommender systems

Source

Journal of the Association for Information Science and Technology. 69(2018) no.9, S.1109-1121

Year

2018

Abstract

Recommender systems are effective in predicting the most suitable products for users, such as movies and books. To facilitate personalized recommendations, the quality of item ratings should be guaranteed. However, a few ratings might not be accurate enough due to the uncertainty of user behavior and are referred to as natural noise. In this article, we present a novel fuzzy clustering-based method for detecting noisy ratings. The entropy of a subset of the original ratings dataset is used to indicate the data-driven uncertainty, and evaluation metrics are adopted to represent the prediction-driven uncertainty. After the repetition of resampling and the execution of a recommendation algorithm, the entropy and evaluation metrics vectors are obtained and are empirically categorized to identify the proportion of the potential noise. Then, the fuzzy C-means-based denoising (FCMD) algorithm is performed to verify the natural noise under the assumption that natural noise is primarily the result of the exceptional behavior of users. Finally, a case study is performed using two real-world datasets. The experimental results show that our proposal outperforms previous proposals and has an advantage in dealing with natural noise.

Content

Vgl.: https://onlinelibrary.wiley.com/doi/10.1002/asi.24036.

Theme

Retrievalalgorithmen

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