Open Access

ARTICLE

Improving Robustness for Tag Recommendation via Self-Paced Adversarial Metric Learning

Zhengshun Fei^1,*, Jianxin Chen¹, Gui Chen², Xinjian Xiang^1,*

1 School of Automation and Electrical Engineering, Zhejiang University of Science and Technology, Hangzhou, 310023, China
2 Bingwu (Ningbo) Intelligent Equipment Co., Ltd., Ningbo, 315600, China

* Corresponding Authors: Zhengshun Fei. Email: ; Xinjian Xiang. Email:

Computers, Materials & Continua 2025, 82(3), 4237-4261. https://doi.org/10.32604/cmc.2025.059262

Received 01 October 2024; Accepted 09 December 2024; Issue published 06 March 2025

Abstract

Tag recommendation systems can significantly improve the accuracy of information retrieval by recommending relevant tag sets that align with user preferences and resource characteristics. However, metric learning methods often suffer from high sensitivity, leading to unstable recommendation results when facing adversarial samples generated through malicious user behavior. Adversarial training is considered to be an effective method for improving the robustness of tag recommendation systems and addressing adversarial samples. However, it still faces the challenge of overfitting. Although curriculum learning-based adversarial training somewhat mitigates this issue, challenges still exist, such as the lack of a quantitative standard for attack intensity and catastrophic forgetting. To address these challenges, we propose a Self-Paced Adversarial Metric Learning (SPAML) method. First, we employ a metric learning model to capture the deep distance relationships between normal samples. Then, we incorporate a self-paced adversarial training model, which dynamically adjusts the weights of adversarial samples, allowing the model to progressively learn from simpler to more complex adversarial samples. Finally, we jointly optimize the metric learning loss and self-paced adversarial training loss in an adversarial manner, enhancing the robustness and performance of tag recommendation tasks. Extensive experiments on the MovieLens and LastFm datasets demonstrate that SPAML achieves F1@3 and NDCG@3 scores of 22% and 32.7% on the MovieLens dataset, and 19.4% and 29% on the LastFm dataset, respectively, outperforming the most competitive baselines. Specifically, F1@3 improves by 4.7% and 6.8%, and NDCG@3 improves by 5.0% and 6.9%, respectively.

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Keywords

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