# Intent Contrastive Learning for Sequential Recommendation

arXiv: https://arxiv.org/abs/2202.02519 · HTML: https://mlsanity.com/paper/2202.02519/

## Key facts

- Cited by 91 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 54 references that are in the map.
- First posted on arXiv 5 Feb 2022.

## Builds on (most cited first, 30 of 30)

- [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)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Unsupervised Learning of Visual Features by Contrasting Cluster Assignments](https://mlsanity.com/paper/2006.09882/) (2020)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://mlsanity.com/paper/2104.08821/) (2021)
- [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)
- [Image-based Recommendations on Styles and Substitutes](https://mlsanity.com/paper/1506.04757/) (2015)
- [Self-supervised Learning: Generative or Contrastive](https://mlsanity.com/paper/2006.08218/) (2020)
- [Neural Graph Collaborative Filtering](https://mlsanity.com/paper/1905.08108/) (2019)
- [Prototypical Contrastive Learning of Unsupervised Representations](https://mlsanity.com/paper/2005.04966/) (2020)
- [Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding](https://mlsanity.com/paper/1809.07426/) (2018)
- [S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization](https://mlsanity.com/paper/2008.07873/) (2020)
- [Self-supervised Graph Learning for Recommendation](https://mlsanity.com/paper/2010.10783/) (2020)
- [Supervised Contrastive Learning for Pre-trained Language Model Fine-tuning](https://mlsanity.com/paper/2011.01403/) (2020)
- [Fusing Similarity Models with Markov Chains for Sparse Sequential Recommendation](https://mlsanity.com/paper/1609.09152/) (2016)
- [Multi-Interest Network with Dynamic Routing for Recommendation at Tmall](https://mlsanity.com/paper/1904.08030/) (2019)
- [Controllable Multi-Interest Framework for Recommendation](https://mlsanity.com/paper/2005.09347/) (2020)
- [An Unsupervised Sentence Embedding Method by Mutual Information Maximization](https://mlsanity.com/paper/2009.12061/) (2020)
- [Continuous-Time Sequential Recommendation with Temporal Graph Collaborative Transformer](https://mlsanity.com/paper/2108.06625/) (2021)
- [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)
- [CosRec: 2D Convolutional Neural Networks for Sequential Recommendation](https://mlsanity.com/paper/1908.09972/) (2019)
- [Lightweight Self-Attentive Sequential Recommendation](https://mlsanity.com/paper/2108.11333/) (2021)
- [Learning Transferrable Parameters for Long-tailed Sequential User Behavior Modeling](https://mlsanity.com/paper/2010.11401/) (2020)
- [Modeling Dynamic Attributes for Next Basket Recommendation](https://mlsanity.com/paper/2109.11654/) (2021)

## Built on by (most cited first, 30 of 91)

- [User-LLM: Efficient LLM Contextualization with User Embeddings](https://mlsanity.com/paper/2402.13598/) (2024)
- [Think Before Recommend: Unleashing the Latent Reasoning Power for Sequential Recommendation](https://mlsanity.com/paper/2503.22675/) (2025)
- [E4SRec: An Elegant Effective Efficient Extensible Solution of Large Language Models for Sequential Recommendation](https://mlsanity.com/paper/2312.02443/) (2023)
- [Diffusion Augmentation for Sequential Recommendation](https://mlsanity.com/paper/2309.12858/) (2023)
- [Plug-in Diffusion Model for Sequential Recommendation](https://mlsanity.com/paper/2401.02913/) (2024)
- [MISSRec: Pre-training and Transferring Multi-modal Interest-aware Sequence Representation for Recommendation](https://mlsanity.com/paper/2308.11175/) (2023)
- [A Survey of Deep Graph Clustering: Taxonomy, Challenge, Application, and Open Resource](https://mlsanity.com/paper/2211.12875/) (2022)
- [Disentangled Contrastive Collaborative Filtering](https://mlsanity.com/paper/2305.02759/) (2023)
- [DiffMM: Multi-Modal Diffusion Model for Recommendation](https://mlsanity.com/paper/2406.11781/) (2024)
- [Graph Masked Autoencoder for Sequential Recommendation](https://mlsanity.com/paper/2305.04619/) (2023)
- [SSLRec: A Self-Supervised Learning Framework for Recommendation](https://mlsanity.com/paper/2308.05697/) (2023)
- [GraphPro: Graph Pre-training and Prompt Learning for Recommendation](https://mlsanity.com/paper/2311.16716/) (2023)
- [Collaboration of Large Language Models and Small Recommendation Models for Device-Cloud Recommendation](https://mlsanity.com/paper/2501.05647/) (2025)
- [SelfGNN: Self-Supervised Graph Neural Networks for Sequential Recommendation](https://mlsanity.com/paper/2405.20878/) (2024)
- [Breaking Determinism: Fuzzy Modeling of Sequential Recommendation Using Discrete State Space Diffusion Model](https://mlsanity.com/paper/2410.23994/) (2024)
- [APGL4SR: A Generic Framework with Adaptive and Personalized Global Collaborative Information in Sequential Recommendation](https://mlsanity.com/paper/2311.02816/) (2023)
- [End-to-end Learnable Clustering for Intent Learning in Recommendation](https://mlsanity.com/paper/2401.05975/) (2024)
- [Diffusion-based Contrastive Learning for Sequential Recommendation](https://mlsanity.com/paper/2405.09369/) (2024)
- [GUESR: A Global Unsupervised Data-Enhancement with Bucket-Cluster Sampling for Sequential Recommendation](https://mlsanity.com/paper/2303.00243/) (2023)
- [FineRec:Exploring Fine-grained Sequential Recommendation](https://mlsanity.com/paper/2404.12975/) (2024)
- [Dual Conditional Diffusion Models for Sequential Recommendation](https://mlsanity.com/paper/2410.21967/) (2024)
- [Exploring news intent and its application: A theory-driven approach](https://mlsanity.com/paper/2312.16490/) (2023)
- [Contrastive Learning on Medical Intents for Sequential Prescription Recommendation](https://mlsanity.com/paper/2408.10259/) (2024)
- [Dual Contrastive Transformer for Hierarchical Preference Modeling in Sequential Recommendation](https://mlsanity.com/paper/2410.22790/) (2024)
- [Scaling Sequential Recommendation Models with Transformers](https://mlsanity.com/paper/2412.07585/) (2024)
- [GRAM: Generative Recommendation via Semantic-aware Multi-granular Late Fusion](https://mlsanity.com/paper/2506.01673/) (2025)
- [A Comprehensive Survey on Self-Supervised Learning for Recommendation](https://mlsanity.com/paper/2404.03354/) (2024)
- [Semantic Codebook Learning for Dynamic Recommendation Models](https://mlsanity.com/paper/2408.00123/) (2024)
- [Bridging User Dynamics: Transforming Sequential Recommendations with Schr"odinger Bridge and Diffusion Models](https://mlsanity.com/paper/2409.10522/) (2024)
- [Modeling Temporal Positive and Negative Excitation for Sequential Recommendation](https://mlsanity.com/paper/2410.22013/) (2024)

## Abstract (arXiv)

Users' interactions with items are driven by various intents (e.g., preparing for holiday gifts, shopping for fishing equipment, etc.).However, users' underlying intents are often unobserved/latent, making it challenging to leverage such latent intents forSequentialrecommendation(SR). To investigate the benefits of latent intents and leverage them effectively for recommendation, we proposeIntentContrastiveLearning(ICL), a general learning paradigm that leverages a latent intent variable into SR. The core idea is to learn users' intent distribution functions from unlabeled user behavior sequences and optimize SR models with contrastive self-supervised learning (SSL) by considering the learned intents to improve recommendation. Specifically, we introduce a latent variable to represent users' intents and learn the distribution function of the latent variable via clustering. We propose to leverage the learned intents into SR models via contrastive SSL, which maximizes the agreement between a view of sequence and its corresponding intent. The training is alternated between intent representation learning and the SR model optimization steps within the generalized expectation-maximization (EM) framework. Fusing user intent information into SR also improves model robustness. Experiments conducted on four real-world datasets demonstrate the superiority of the proposed learning paradigm, which improves performance, and robustness against data sparsity and noisy interaction issues.