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January 14, 2026Information0 citationsOpen Access

mdm-gansa: a novel shilling attack model for recommender systems

MDM-GANSA: A Multi-Distribution Generative Shilling Attack for Recommender Systems

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Authors

QZQuanqiang ZhouXZXiaoyue ZhangXZXi Zhao

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Overview

Proposed model employs autoencoder for data-driven dependency modeling, enhancing the realism of fake profiles.

Key Points

  • To develop a sophisticated model, MDM-GANSA, for generating realistic fake user profiles in shilling attacks on recommender systems.
  • Introduces dynamic adaptive noise strategy with a weight predictor network.
  • Utilizes an autoencoder for data-driven dependency modeling.
  • Implements a two-stage generative architecture with fine-grained loss constraints.
  • MDM-GANSA significantly outperforms baseline models in attack effectiveness and stealthiness.
  • Demonstrates improved realism in generated fake user profiles.

Cite This Study

Zhou et al. (2026) studied this question.

synapsesocial.com/papers/6966f31d13bf7a6f02c00d0chttps://doi.org/10.3390/info17010077
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