A simplistic model for spammers detection in social recommender systems

Hemza Loucif

International Journal of Business Information Systems, 2025, vol. 49, issue 2, 199-217

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. (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:ids:ijbisy:v:49:y:2025:i:2:p:199-217

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