An Efficient Approach to Manage Natural Noises in Recommender Systems

Luo, Chenhong; Wang, Yong; Li, Bo; Liu, Hanyang; Wang, Pengyu; Zhang, Leo Yu

doi:10.3390/a16050228

Open AccessArticle

An Efficient Approach to Manage Natural Noises in Recommender Systems

by

Chenhong Luo

¹,

Yong Wang

^1,2,*

,

Bo Li

¹,

Hanyang Liu

¹,

Pengyu Wang

² and

Leo Yu Zhang

^3,*

¹

College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

²

Key Laboratory of Data Science and Complex System Management, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

³

School of Information and Communication Technology, Griffith University, Southport, QLD 4215, Australia

^*

Authors to whom correspondence should be addressed.

Algorithms 2023, 16(5), 228; https://doi.org/10.3390/a16050228

Submission received: 15 February 2023 / Revised: 7 April 2023 / Accepted: 25 April 2023 / Published: 27 April 2023

(This article belongs to the Special Issue New Trends in Algorithms for Intelligent Recommendation Systems)

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Abstract

Recommender systems search the underlying preferences of users according to their historical ratings and recommend a list of items that may be of interest to them. Rating information plays an important role in revealing the true tastes of users. However, previous research indicates that natural noises may exist in the historical ratings and mislead the recommendation results. To deal with natural noises, different methods have been proposed, such as directly removing noises, correcting noise by re-predicting, or using additional information. However, these methods introduce some new problems, such as data sparsity and introducing new sources of noise. To address the problems, we present a new approach to managing natural noises in recommendation systems. Firstly, we provide the detection criteria for natural noises based on the classifications of users and items. After the noises are detected, we correct them with threshold values weighted by probabilities. Experimental results show that the proposed method can effectively correct natural noise and greatly improve the quality of recommendations.

Keywords: recommender system; natural noise; collaborative filtering; data sparsity

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MDPI and ACS Style

Luo, C.; Wang, Y.; Li, B.; Liu, H.; Wang, P.; Zhang, L.Y. An Efficient Approach to Manage Natural Noises in Recommender Systems. Algorithms 2023, 16, 228. https://doi.org/10.3390/a16050228

AMA Style

Luo C, Wang Y, Li B, Liu H, Wang P, Zhang LY. An Efficient Approach to Manage Natural Noises in Recommender Systems. Algorithms. 2023; 16(5):228. https://doi.org/10.3390/a16050228

Chicago/Turabian Style

Luo, Chenhong, Yong Wang, Bo Li, Hanyang Liu, Pengyu Wang, and Leo Yu Zhang. 2023. "An Efficient Approach to Manage Natural Noises in Recommender Systems" Algorithms 16, no. 5: 228. https://doi.org/10.3390/a16050228

APA Style

Luo, C., Wang, Y., Li, B., Liu, H., Wang, P., & Zhang, L. Y. (2023). An Efficient Approach to Manage Natural Noises in Recommender Systems. Algorithms, 16(5), 228. https://doi.org/10.3390/a16050228

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

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An Efficient Approach to Manage Natural Noises in Recommender Systems

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