研究虚假用户对推荐系统的影响，发现其会严重破坏系统稳定性。
The Role of Fake Users in Sequential Recommender Systems
- 通过模拟不同行为的虚假用户，测试推荐系统表现
- 虚假用户使RLS指标降至接近零，而NDCG变化不大
- 提醒需构建更抗干扰的推荐系统，适合安全与风控研究者
序列推荐系统（SRS）广泛用于建模用户随时间的行为，但其鲁棒性仍缺乏深入研究。本文通过实证分析，评估虚假用户（随机互动、追随热门或冷门内容、专注单一类型）在真实场景下对SRS性能的影响。我们在多个数据集上测试两种SRS模型，采用NDCG和RLS等标准指标进行评估。结果显示，尽管传统指标如NDCG保持相对稳定，但虚假用户的出现显著恶化了RLS指标，常导致其降至接近零。这揭示了虚假用户对训练数据的潜在危害，强调了开发更具鲁棒性的SRS以应对各类对抗攻击的必要性。
原文摘要 · Abstract (English)
Sequential Recommender Systems (SRSs) are widely used to model user behavior over time, yet their robustness remains an under-explored area of research. In this paper, we conduct an empirical study to assess how the presence of fake users, who engage in random interactions, follow popular or unpopular items, or focus on a single genre, impacts the performance of SRSs in real-world scenarios. We evaluate two SRS models across multiple datasets, using established metrics such as Normalized Discounted Cumulative Gain (NDCG) and Rank Sensitivity List (RLS) to measure performance. While traditional metrics like NDCG remain relatively stable, our findings reveal that the presence of fake users severely degrades RLS metrics, often reducing them to near-zero values. These results highlight the need for further investigation into the effects of fake users on training data and emphasize the importance of developing more resilient SRSs that can withstand different types of adversarial attacks.
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