Title: A simplistic model for spammers detection in social recommender systems

Authors: Hemza Loucif

Addresses: Computer Science Department, Laboratory of Informatics and its Applications of M'sila (LIAM), M'sila University, M'sila, Algeria

Abstract: The dramatic growth of social networks and the diversity of their user base give the web business companies an unprecedented opportunity to stay well ahead of competition. Companies like Amazon, Alibaba, and other web business leaders are still investing huge amounts of money in order to improve their social network-based online recommender systems. Detecting and filtering spammers (aka fake recommenders) who post messages containing malicious commercial URLs in these environments is becoming a serious issue that the social network analysis community must confront. In this paper, we introduce a simplistic and effective machine learning based classifier to detect spammers. A multi-layer perceptron (MLP) with backpropagation training constitutes the core of the classifier. Using a public dataset of real-world social networks, the experiments demonstrated the possibility of reaching high accuracy levels with no more than two hidden layers.

Keywords: spammer detection; recommender systems; back propagation; multi-layer perceptron; MLP.

DOI: 10.1504/IJBIS.2025.146599

International Journal of Business Information Systems, 2025 Vol.49 No.2, pp.199 - 217

Received: 10 Mar 2021
Accepted: 07 Oct 2021
Published online: 06 Jun 2025 *

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