# Uniform Sequence Better: Time Interval Aware Data Augmentation for Sequential Recommendation

arXiv: https://arxiv.org/abs/2212.08262 · HTML: https://mlsanity.com/paper/2212.08262/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 34 references that are in the map.
- First posted on arXiv 16 Dec 2022.

## Builds on (most cited first, 20 of 20)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Self-Attentive Sequential Recommendation](https://mlsanity.com/paper/1808.09781/) (2018)
- [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://mlsanity.com/paper/2002.02126/) (2020)
- [BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer](https://mlsanity.com/paper/1904.06690/) (2019)
- [Session-based Recommendations with Recurrent Neural Networks](https://mlsanity.com/paper/1511.06939/) (2015)
- [S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization](https://mlsanity.com/paper/2008.07873/) (2020)
- [Fusing Similarity Models with Markov Chains for Sparse Sequential Recommendation](https://mlsanity.com/paper/1609.09152/) (2016)
- [RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms](https://mlsanity.com/paper/2011.01731/) (2020)
- [Contrastive Learning for Representation Degeneration Problem in Sequential Recommendation](https://mlsanity.com/paper/2110.05730/) (2021)
- [Inferring Networks of Substitutable and Complementary Products](https://mlsanity.com/paper/1506.08839/) (2015)
- [Contrastive Self-supervised Sequential Recommendation with Robust Augmentation](https://mlsanity.com/paper/2108.06479/) (2021)
- [Self-attention with Functional Time Representation Learning](https://mlsanity.com/paper/1911.12864/) (2019)
- [RecBole 2.0: Towards a More Up-to-Date Recommendation Library](https://mlsanity.com/paper/2206.07351/) (2022)
- [Context-aware Sequential Recommendation](https://mlsanity.com/paper/1609.05787/) (2016)
- [Future Data Helps Training: Modeling Future Contexts for Session-based Recommendation](https://mlsanity.com/paper/1906.04473/) (2019)
- [Context-Aware Attention-Based Data Augmentation for POI Recommendation](https://mlsanity.com/paper/2106.15984/) (2021)
- [Modeling Interdependent and Periodic Real-World Action Sequences](https://mlsanity.com/paper/1802.09148/) (2018)
- [Exploiting Session Information in BERT-based Session-aware Sequential Recommendation](https://mlsanity.com/paper/2204.10851/) (2022)
- [CmnRec: Sequential Recommendations with Chunk-accelerated Memory Network](https://mlsanity.com/paper/2004.13401/) (2020)

## Built on by (most cited first, 16 of 16)

- [CoMaPOI: A Collaborative Multi-Agent Framework for Next POI Prediction Bridging the Gap Between Trajectory and Language](https://mlsanity.com/paper/2505.23837/) (2025)
- [EasyDGL: Encode, Train and Interpret for Continuous-time Dynamic Graph Learning](https://mlsanity.com/paper/2303.12341/) (2023)
- [A Comprehensive Survey on Self-Supervised Learning for Recommendation](https://mlsanity.com/paper/2404.03354/) (2024)
- [Parallel Ranking of Ads and Creatives in Real-Time Advertising Systems](https://mlsanity.com/paper/2312.12750/) (2023)
- [Unveiling Discrete Clues: Superior Healthcare Predictions for Rare Diseases](https://mlsanity.com/paper/2501.16373/) (2025)
- [UniRec: A Dual Enhancement of Uniformity and Frequency in Sequential Recommendations](https://mlsanity.com/paper/2406.18470/) (2024)
- [Temporal Linear Item-Item Model for Sequential Recommendation](https://mlsanity.com/paper/2412.07382/) (2024)
- [ID Embedding as Subtle Features of Content and Structure for Multimodal Recommendation](https://mlsanity.com/paper/2311.05956/) (2023)
- [eSASRec: Enhancing Transformer-based Recommendations in a Modular Fashion](https://mlsanity.com/paper/2508.06450/) (2025)
- [SS4Rec: Continuous-Time Sequential Recommendation with State Space Models](https://mlsanity.com/paper/2502.08132/) (2025)
- [Not Just What, But When: Integrating Irregular Intervals to LLM for Sequential Recommendation](https://mlsanity.com/paper/2507.23209/) (2025)
- [Enhancing Time Awareness in Generative Recommendation](https://mlsanity.com/paper/2509.13957/) (2025)
- [Adapting Job Recommendations to User Preference Drift with Behavioral-Semantic Fusion Learning](https://mlsanity.com/paper/2407.00082/) (2024)
- [Revisiting Graph Projections for Effective Complementary Product Recommendation](https://mlsanity.com/paper/2506.09209/) (2025)
- [Fusion and Alignment Enhancement with Large Language Models for Tail-item Sequential Recommendation](https://mlsanity.com/paper/2604.03688/) (2026)
- [TME-PSR: Time-aware, Multi-interest, and Explanation Personalization for Sequential Recommendation](https://mlsanity.com/paper/2604.09439/) (2026)

## Abstract (arXiv)

Sequential recommendation is an important task to predict the next-item to access based on a sequence of interacted items. Most existing works learn user preference as the transition pattern from the previous item to the next one, ignoring the time interval between these two items. However, we observe that the time interval in a sequence may vary significantly different, and thus result in the ineffectiveness of user modeling due to the issue of preference drift. In fact, we conducted an empirical study to validate this observation, and found that a sequence with uniformly distributed time interval (denoted as uniform sequence) is more beneficial for performance improvement than that with greatly varying time interval. Therefore, we propose to augment sequence data from the perspective of time interval, which is not studied in the literature. Specifically, we design five operators (Ti-Crop, Ti-Reorder, Ti-Mask, Ti-Substitute, Ti-Insert) to transform the original non-uniform sequence to uniform sequence with the consideration of variance of time intervals. Then, we devise a control strategy to execute data augmentation on item sequences in different lengths. Finally, we implement these improvements on a state-of-the-art model CoSeRec and validate our approach on four real datasets. The experimental results show that our approach reaches significantly better performance than the other 11 competing methods. Our implementation is available: https://github.com/KingGugu/TiCoSeRec.