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.

翻译：序列推荐系统（SRSs）被广泛用于建模用户随时间变化的行为，但其鲁棒性仍是一个研究不足的领域。本文通过一项实证研究，评估了虚假用户（他们进行随机交互、关注流行或非流行项目，或专注于单一类型）的存在，在现实场景中如何影响SRSs的性能。我们在多个数据集上评估了两个SRS模型，使用归一化折损累计增益（NDCG）和排序敏感度列表（RLS）等既定指标来衡量性能。虽然NDCG等传统指标保持相对稳定，但我们的研究结果表明，虚假用户的存在会严重降低RLS指标，常常使其降至接近零值。这些结果凸显了进一步研究虚假用户对训练数据影响的必要性，并强调了开发更具弹性、能够抵御不同类型对抗性攻击的SRSs的重要性。