Paper Info
Title
Critique on Natural Noise in Recommender Systems
Abstract
AbstractRecommender systems have been upgraded, tested, and applied in many, often incomparable ways. In attempts to diligently understand user behavior in certain environments, those systems have been frequently utilized in domains like e-commerce, e-learning, and tourism. Their increasing need and popularity have allowed the existence of numerous research paths on major issues like data sparsity, cold start, malicious noise, and natural noise, which immensely limit their performance. It is typical that the quality of the data that fuel those systems should be extremely reliable. Inconsistent user information in datasets can alter the performance of recommenders, albeit running advanced personalizing algorithms. The consequences of this can be costly as such systems are employed in abundant online businesses. Successfully managing these inconsistencies results in more personalized user experiences. In this article, the previous works conducted on natural noise management in recommender datasets are thoroughly analyzed. We adequately explore the ways in which the proposed methods measure improved performances and touch on the different natural noise management techniques and the attributes of the solutions. Additionally, we test the evaluation methods employed to assess the approaches and discuss several key gaps and other improvements the field should realize in the future. Our work considers the likelihood of a modern research branch on natural noise management and recommender assessment.
Year
DOI
Venue
2021
10.1145/3447780
ACM Transactions on Knowledge Discovery from Data
Keywords
DocType
Volume
Recommender systems, natural noise management, evaluation metrics
Journal
15
Issue
ISSN
Citations 
5
1556-4681
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Wissam Al Jurdi100.34
Jacques Bou Abdo200.34
Jacques Demerjian32912.60
Abdallah Makhoul429936.48