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

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.

How TikTok leads users to flow experience: investigating the effects of technology affordances with user experience level and video length as moderators

“…Perceived recommendation accuracy, which describes the degree to which recommended content is accurately customized to a user's preferences, represents the focus of most relevant studies (Wang et al, 2020;Roudposhti et al, 2018;Zhu et al, 2018). TikTok's famously hyperaccurate algorithms have attracted substantial attention.…”

“…Third, the current study contributes to the recommendation system research by evaluating the recommendation algorithm from the user's perspective. Most studies of recommendation systems are design science studies that introduce complex algorithms to promote the accuracy of recommendations (Wang et al, 2020;Roudposhti et al, 2018; The effects of technology affordances Zhu et al, 2018). Following the appearance of problems such as the "filter bubble" and "information cocoons," recommendation serendipity has become a sought-after property, with many researchers from the design science field introducing advanced algorithms to inject serendipity into recommendation systems (Wang et al, 2020;Cai et al, 2021).…”

Application of Improved K-Means Algorithm in Collaborative Recommendation System

“…Zhu et al proposed a fuzzy clustering-based method that evaluates the prediction-driven uncertainty and classifies based on existing data. Experimental results show that the method outperforms traditional collaborative filtering recommendation algorithms [15]. Chakraborty et al addressed the problem of large bias in clustering results caused by initial guesses of clustering centers by combining the K-means algorithm with a volume metric algorithm and genetic arithmetic as a way to predict the optimal value of initial clustering centers.…”