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A Fuzzy Linguistic Approach-Based Non-malicious Noise Detection Algorithm for Recommendation System
Sharon Moses J,
Published in Springer Science and Business Media LLC
Volume: 20
Issue: 8
Pages: 2368 - 2382
In the world of web, recommendation system plays a vital role in predicting the user desirable item from a plethora of items. Since the recommendation system generates recommendations by processing the user ratings, the presence of irrelevant or noisy ratings will affect the accuracy of the recommendation system. Malicious users induce noise into the system in order to popularize or to de-promote a particular item which might result in reduced accuracy of recommendations. In addition to malicious users, genuine users offer noisy ratings to the items unknowingly. Despite several of the existing methods to detect and resolve the malicious noise, only few address the existence of natural or non-malicious noise in the recommendation system. Detecting natural noise is not as precise as dealing with the malicious noise. Since natural noise is caused by genuine user, the system needs to classify each rating from the perspective of user rating. In this paper, a fuzzy linguistic approach-based natural noise detection algorithm is proposed to address the uncertainty in detecting the non-malicious noise. The proposed detection algorithm is highly efficient as indicated by our experiments that evaluate noise-induced real-world data sets. © 2018, Taiwan Fuzzy Systems Association and Springer-Verlag GmbH Germany, part of Springer Nature.
About the journal
JournalData powered by TypesetInternational Journal of Fuzzy Systems
PublisherData powered by TypesetSpringer Science and Business Media LLC
Open AccessNo