International Journal of Fuzzy Systems

, Volume 20, Issue 8, pp 2368–2382 | Cite as

A Fuzzy Linguistic Approach-Based Non-malicious Noise Detection Algorithm for Recommendation System

  • J. Sharon Moses
  • L. D. Dhinesh BabuEmail author


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.


Natural noise Non-malicious noise Recommender system Fuzzy linguistic term set Fuzzy detection algorithm 


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Copyright information

© Taiwan Fuzzy Systems Association and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Information Technology and EngineeringVIT UniversityVelloreIndia

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