Sequential recommendation (SR) has received much attention and made promising progress in the past few years due to its high alignment with real recommendation scenarios. It models users' preferences and behavior patterns from their historical behavior sequences and provides personalized recommendations. However, the widespread problem of data sparsity limits the performance of sequential recommendation models. To tackle this, data augmentation (DA) provides a feasible solution by improving the quantity, quality, or diversity of the training samples without the need for additional data collection. In this survey, we present a systematic and timely review of research efforts on data augmentation for sequential recommendation. We start by providing a clear formulation of the problem and task. Then, we develop a unified taxonomy that categorizes existing augmentation methodologies regarding their augmentation objects and principles. Next, we conduct a comparative discussion on the advantages and disadvantages of different categories, supplemented with quantitative performance evaluations, time-complexity analyses, and visual case studies of representative methods, aiming to provide actionable guidance for the selection and development of augmentation methods in real-world scenarios. Finally, we present the future research directions and summarize this survey.

Intent-Aware Sequential Recommendation (ISR) predicts next-item interactions by capturing the implicit intents behind users’ historical behavior sequences. Although existing ISR methods have achieved promising performance, we argue that two smoothness issues remain insufficiently addressed. First, existing methods often overemphasize short-term intents, making it difficult to smoothly balance users’ short- and long-term preferences. Second, hard positive–negative boundaries in intent contrastive learning may introduce misleading contrastive signals and disrupt the semantic continuity of user behaviors. To tackle these issues, we propose Smoothed Intent Learning for Sequential Recommendation (SILRec) , which improves the smoothness of short- and long-term intents modeling as well as intent contrastive samples. Specifically, we design an SILRec encoder with a multi-scale exponential moving average module and a frequency-domain module to model user intents across different temporal and spectral patterns. We further introduce boundary-smoothed contrastive learning based on representation interpolation, which constructs smoother transition regions between intent representations and alleviates the rigidity of hard contrastive sample boundaries. Comprehensive experiments on 6 conventional real-world datasets with 13 competitors demonstrate the effectiveness of SILRec. We further conduct an additional evaluation on the KuaiRec short-video dataset to examine its applicability to more dynamic recommendation scenarios. Additional analyses from multiple perspectives, including controlled noise perturbation, long-tail performance, and intent visualization, provide further evidence for the proposed design. Our codes and datasets are available at https://github.com/syf1844803351/SILRec .

Data augmentation has become a promising method of mitigating data sparsity in sequential recommendation.Existing methods generate new yet effective data during model training to improve performance.However, deploying them requires retraining, architecture modification, or introducing additional learnable parameters.These steps are time-consuming and costly for well-trained models, especially when the model scale becomes large.In this work, we explore the test-time augmentation (TTA) for sequential recommendation, which augments the inputs during the model inference and then aggregates the model's predictions for augmented data to improve final accuracy.It avoids significant time and cost overhead from the previously mentioned steps.We first experimentally disclose the potential of existing augmentation operators for TTA and find that the Mask and Substitute consistently achieve better performance.Further analysis reveals that these two operators are effective because they retain the original sequential pattern while adding appropriate perturbations.Meanwhile, we argue that these two operators still face time-consuming item selection or interference information from mask tokens.Based on the analysis and limitations, we present TNoise and TMask.The former injects uniform noise into the original representation, avoiding the computational overhead of item selection.The latter blocks

Logical reasoning-based recommendation methods employ logical rules to mitigate the adverse effects of noise items in short interaction sequences on recommendation accuracy.However, there are two problems with existing methods: 1) As the length of the interaction sequence increases, introducing more noise items exacerbates the negative impact on logical reasoning, thereby reducing the accuracy of these methods.2) They are often dominated by the user's single primary interest, which prevents simultaneous consideration of users' multiple-aspect interests in long sequences.To address these issues, we propose a novel dEnoising Multi-Interestaware Logical rEasoning (EMILE) method for long-sequence recommendation.Specifically, we design a logical rule-based interest extractor that enhances the importance of preferred items in constructing user interests while minimizing the negative impact of disliked items.This extractor effectively mitigates the adverse effects of noise items in long interaction sequences.Furthermore, we propose a novel multi-interest learning strategy that optimizes two new objective functions-interest probability distribution contrastive loss and interest logical reasoning contrastive loss-to ensure the model simultaneously considers multiple-aspect interests.These two objective functions require that the target item is more * Corresponding authors.

Recommendation unlearning is an emerging task to erase the influences of user-specified data from a trained recommendation model. Most existing research follows the paradigm of partitioning the original dataset into multi-fold and then retraining corresponding sub-models while those influences are totally removed. Despite the effectiveness, two key problems remain unexplored: (i) Existing work becomes inefficient and computationally expensive to retrain all sub-models, especially when facing large amounts of unlearning data. (ii) User preferences are dynamically changing. If users express negative opinions on some interacted items they used to prefer, how can we adaptively erase the outdated preferences behind such transformation from the trained model? Although these unlearning data contain outdated information, there is still a lot of helpful knowledge worth preserving. Existing methods ignore this preservation during unlearning and may remove all the knowledge in the interactions, compromising the final performance. In light of these limitations, we propose a novel unlearning framework called GFEraser, which transforms the unlearning into an efficient guided filtering process to avoid time-consuming retraining and retain beneficial knowledge. Specifically, we develop an intra-user negative sampling strategy to learn the outdated preferences that need to be erased. Under the guidance of differential maximization agreement and attention-based fusion module, the original representations are adaptively filtered and aggregated based on the learned preferences. Besides, we leverage contrastive learning to preserve the invariant user preferences, maintaining the final performance. Finally, we devise a new metric called Ranking Decrease Rate to evaluate the unlearning effect. Experimental results demonstrate that GFEraser can maintain reliable recommendation performance while achieving efficient outdated preferences unlearning, up to 37 \(\times\) acceleration.

Sequential recommendation aims to provide users with personalized suggestions based on their historical interactions. When training sequential models, padding is a widely adopted technique for two main reasons: 1) The vast majority of models can only handle fixed-length sequences; 2) Batch-based training needs to ensure that the sequences in each batch have the same length. The special value 0 is usually used as the padding content, which does not contain the actual information and is ignored in the model calculations. This common-sense padding strategy leads us to a problem that has never been explored in the recommendation field: Can we utilize this idle input space by padding other content to improve model performance and training efficiency further?

Sequential recommendation has now been more widely studied, characterized by its well-consistency with real-world recommendation situations. Most existing works model user preference as the transition pattern from the previous item to the next, ignoring the time interval between these two items. However, we find that the time intervals in different sequences may vary significantly and thus result in the ineffectiveness of user modeling due to the issue ofpreference drift. Thus we propose an assumption that a sequence with uniformly distributed time intervals (denoted as uniform sequence) is more beneficial for preference learning than that with greatly varying time intervals. We then conduct an empirical study on four real datasets and the results support this assumption. Therefore, we advocate to augment sequence data from the perspective of time intervals, which is not studied in the literature. Specifically, we design five operators (Ti-Crop, Ti-CateReorder, Ti-Mask, Ti-Substitute, Ti-Insert) to transform the original non-uniform sequence to uniform sequence with the consideration of time intervals. Then, we devise a control strategy to execute data augmentation on item sequences in different lengths and a looseness range to ensure the generalization (or diversity) of generated data. Finally, we implement these improvements on a state-of-the-art model CoSeRec and proposeTimeInterval AwareCoSeRec(TiCoSeRec). Experimental results on four datasets demonstrate that TiCoSeRec achieves significantly better performance than other 11 counterparts recommendation techniques.