# Contrastive Learning for Sequential Recommendation

arXiv: https://arxiv.org/abs/2010.14395 · HTML: https://mlsanity.com/paper/2010.14395/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Oct 2020.

## Built on by (most cited first, 13 of 13)

- [Contrastive Self-supervised Sequential Recommendation with Robust Augmentation](https://mlsanity.com/paper/2108.06479/) (2021)
- [Augmenting Sequential Recommendation with Pseudo-Prior Items via Reversely Pre-training Transformer](https://mlsanity.com/paper/2105.00522/) (2021)
- [One4all User Representation for Recommender Systems in E-commerce](https://mlsanity.com/paper/2106.00573/) (2021)
- [MAP: A Model-agnostic Pretraining Framework for Click-through Rate Prediction](https://mlsanity.com/paper/2308.01737/) (2023)
- [RecGPT: Generative Personalized Prompts for Sequential Recommendation via ChatGPT Training Paradigm](https://mlsanity.com/paper/2404.08675/) (2024)
- [Multi-Sample based Contrastive Loss for Top-k Recommendation](https://mlsanity.com/paper/2109.00217/) (2021)
- [Improving Contrastive Learning with Model Augmentation](https://mlsanity.com/paper/2203.15508/) (2022)
- [Contrastive Pre-training for Imbalanced Corporate Credit Ratings](https://mlsanity.com/paper/2102.12580/) (2021)
- [Self Contrastive Learning for Session-based Recommendation](https://mlsanity.com/paper/2306.01266/) (2023)
- [Learnable Model Augmentation Self-Supervised Learning for Sequential Recommendation](https://mlsanity.com/paper/2204.10128/) (2022)
- [UserBERT: Modeling Long- and Short-Term User Preferences via Self-Supervision](https://mlsanity.com/paper/2202.07605/) (2022)
- [A Social-aware Gaussian Pre-trained Model for Effective Cold-start Recommendation](https://mlsanity.com/paper/2311.15790/) (2023)
- [Multi-Item-Query Attention for Stable Sequential Recommendation](https://mlsanity.com/paper/2509.24424/) (2025)

## Abstract (arXiv)

Sequential recommendation methods play a crucial role in modern recommender systems because of their ability to capture a user's dynamic interest from her/his historical interactions. Despite their success, we argue that these approaches usually rely on the sequential prediction task to optimize the huge amounts of parameters. They usually suffer from the data sparsity problem, which makes it difficult for them to learn high-quality user representations. To tackle that, inspired by recent advances of contrastive learning techniques in the computer version, we propose a novel multi-task model called Contrastive Learning for Sequential Recommendation~(CL4SRec). CL4SRec not only takes advantage of the traditional next item prediction task but also utilizes the contrastive learning framework to derive self-supervision signals from the original user behavior sequences. Therefore, it can extract more meaningful user patterns and further encode the user representation effectively. In addition, we propose three data augmentation approaches to construct self-supervision signals. Extensive experiments on four public datasets demonstrate that CL4SRec achieves state-of-the-art performance over existing baselines by inferring better user representations.