A Survey on Robust Sequential Recommendation: Fundamentals, Challenges, Taxonomy, and Future Directions

In the era of information overload, sequential recommender systems (SRSs) have become indispensable tools for modeling users’ dynamic preferences, assisting personalized decision-making and information filtering, and thus attracting significant research and industrial attention. Conventional SRSs operate on a critical assumption that every input interaction sequence is reliably matched with the target subsequent interaction. However, this assumption is frequently violated in practice: real-world user behaviors are often driven by extrinsic motivations—such as behavioral randomness, contextual influences, and malicious attacks—which introduce perturbations into interaction sequences. These perturbations result in mismatched input-target pairs, termed unreliable instances, which corrupt sequential patterns, mislead model training and inference, and ultimately degrade recommendation accuracy. To mitigate these issues, the study of Robust Sequential Recommenders (RSRs) has emerged as a focal point. This survey provides the first systematic review of advances in RSR research. We begin with a thorough analysis of unreliable instances, detailing their causes, manifestations, and adverse impacts. We then delineate the unique challenges of RSRs, which are absent in non-sequential settings and general denoising tasks. Subsequently, we present a holistic taxonomy of RSR methodologies and a systematic comparative analysis based on eight key properties, critically assessing the strengths and limitations of existing approaches. We also summarize standard evaluation metrics and benchmarks. Finally, we identify open issues and discuss promising future research directions. To support the community, we maintain a rich repository of RSR resources at https://github.com/AlchemistYT/Awesome-RSRs