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

TL;DR

The paper addresses the vulnerability of collaborative filtering recommender systems to outliers and manipulated ratings. It proposes ECORS, an ensembled clustering framework that builds a user-user similarity matrix from ratings and applies multiple clustering algorithms (K-means, K-medoids, DBSCAN, Divisive) to detect local and global outliers. The study demonstrates improved accuracy and ranking performance on the Movielens ML-1M dataset, evidenced by better MAE, precision, recall, and F-measure compared with baselines. The method enhances robustness of recommendations to noisy and adversarial data, with practical implications for scalable, trusted recommender systems.

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.

Figures (6)

• Figure 1: Outlier
• Figure 2: Flow Chart of Method
• Figure 3: Top-N Neighbor vs. MAE.
• Figure 4: Top-N Neighbor vs. Precision.
• Figure 5: Top-N Neighbor vs. Recall.