ECORS: An Ensembled Clustering Approach to Eradicate The Local And Global Outlier In Collaborative Filtering Recommender System

TL;DR

This paper introduces ECORS, an ensemble clustering method that effectively detects local and global outliers in collaborative filtering recommender systems, enhancing their robustness and accuracy.

Contribution

It proposes a novel ensemble clustering approach using user-user matrices to improve outlier detection in recommender systems, addressing limitations of existing methods.

Findings

Significantly improves outlier detection accuracy

Effectively identifies both local and global outliers

Enhances recommender system robustness

Abstract

Recommender systems are designed to suggest items based on user preferences, helping users navigate the vast amount of information available on the internet. Given the overwhelming content, outlier detection has emerged as a key research area in recommender systems. It involves identifying unusual or suspicious patterns in user behavior. However, existing studies in this field face several challenges, including the limited universality of algorithms, difficulties in selecting users, and a lack of optimization. In this paper, we propose an approach that addresses these challenges by employing various clustering algorithms. Specifically, we utilize a user-user matrix-based clustering technique to detect outliers. By constructing a user-user matrix, we can identify suspicious users in the system. Both local and global outliers are detected to ensure comprehensive analysis. Our experimental results demonstrate that this approach significantly improves the accuracy of outlier detection in recommender systems.