Investigating the Robustness of Sequential Recommender Systems Against Training Data Perturbations: an Empirical Study
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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.
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