Sequential Recommender Systems (SRSs) have been widely used to model user behavior over time, but their robustness in the face of perturbations to training data is a critical issue. In this paper, we conduct an empirical study to investigate the effects of removing items at different positions within a temporally ordered sequence. We evaluate two different SRS models on multiple datasets, measuring their performance using Normalized Discounted Cumulative Gain (NDCG) and Rank Sensitivity List metrics. Our results demonstrate that removing items at the end of the sequence significantly impacts performance, with NDCG decreasing up to 60\%, while removing items from the beginning or middle has no significant effect. These findings highlight the importance of considering the position of the perturbed items in the training data and shall inform the design of more robust SRSs.

翻译：序列推荐系统（SRSs）已被广泛用于建模用户行为随时间演变的规律，但其在训练数据扰动下的鲁棒性是一个关键问题。本文通过实证研究，探究从时序序列中移除不同位置项目的影响。我们在多个数据集上评估两种不同的SRS模型，采用归一化折损累计增益（NDCG）和排名敏感度列表指标衡量模型表现。结果表明，移除序列末尾项目会显著影响性能（NDCG下降高达60%），而移除开头或中间位置项目则未产生明显影响。这一发现揭示了训练数据中扰动项目位置的重要性，将为设计更鲁棒的SRS提供指导。