# CauseRec: Counterfactual User Sequence Synthesis for Sequential Recommendation

arXiv: https://arxiv.org/abs/2109.05261 · HTML: https://mlsanity.com/paper/2109.05261/

## Key facts

- Cited by 46 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2021.

## Built on by (most cited first, 30 of 46)

- [Filter-enhanced MLP is All You Need for Sequential Recommendation](https://mlsanity.com/paper/2202.13556/) (2022)
- [Compositional Temporal Grounding with Structured Variational Cross-Graph Correspondence Learning](https://mlsanity.com/paper/2203.13049/) (2022)
- [Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AI](https://mlsanity.com/paper/2111.06061/) (2021)
- [Diffusion Augmentation for Sequential Recommendation](https://mlsanity.com/paper/2309.12858/) (2023)
- [Target-aware Abstractive Related Work Generation with Contrastive Learning](https://mlsanity.com/paper/2205.13339/) (2022)
- [End-to-End Modeling via Information Tree for One-Shot Natural Language Spatial Video Grounding](https://mlsanity.com/paper/2203.08013/) (2022)
- [Graph Masked Autoencoder for Sequential Recommendation](https://mlsanity.com/paper/2305.04619/) (2023)
- [Disentangled Knowledge Tracing for Alleviating Cognitive Bias](https://mlsanity.com/paper/2503.02539/) (2025)
- [KEEP: An Industrial Pre-Training Framework for Online Recommendation via Knowledge Extraction and Plugging](https://mlsanity.com/paper/2208.10174/) (2022)
- [Why Do We Click: Visual Impression-aware News Recommendation](https://mlsanity.com/paper/2109.12651/) (2021)
- [Contrastive Learning with Positive-Negative Frame Mask for Music Representation](https://mlsanity.com/paper/2203.09129/) (2022)
- [Intelligent Request Strategy Design in Recommender System](https://mlsanity.com/paper/2206.12296/) (2022)
- [Device-Cloud Collaborative Recommendation via Meta Controller](https://mlsanity.com/paper/2207.03066/) (2022)
- [Towards Out-of-Distribution Sequential Event Prediction: A Causal Treatment](https://mlsanity.com/paper/2210.13005/) (2022)
- [Accurate Action Recommendation for Smart Home via Two-Level Encoders and Commonsense Knowledge](https://mlsanity.com/paper/2208.06089/) (2022)
- [Click-through Rate Prediction with Auto-Quantized Contrastive Learning](https://mlsanity.com/paper/2109.13921/) (2021)
- [Contrastive Balancing Representation Learning for Heterogeneous Dose-Response Curves Estimation](https://mlsanity.com/paper/2403.14232/) (2024)
- [MC2-SF: Slow-Fast Learning for Mobile-Cloud Collaborative Recommendation](https://mlsanity.com/paper/2109.12314/) (2021)
- [CorefDRE: Document-level Relation Extraction with coreference resolution](https://mlsanity.com/paper/2202.10744/) (2022)
- [3D Magic Mirror: Clothing Reconstruction from a Single Image via a Causal Perspective](https://mlsanity.com/paper/2204.13096/) (2022)
- [Learning Instrumental Variable from Data Fusion for Treatment Effect Estimation](https://mlsanity.com/paper/2208.10912/) (2022)
- [Do We Fully Understand Students' Knowledge States? Identifying and Mitigating Answer Bias in Knowledge Tracing](https://mlsanity.com/paper/2308.07779/) (2023)
- [Quantitatively Measuring and Contrastively Exploring Heterogeneity for Domain Generalization](https://mlsanity.com/paper/2305.15889/) (2023)
- [CSRec: Rethinking Sequential Recommendation from A Causal Perspective](https://mlsanity.com/paper/2409.05872/) (2024)
- [Counterfactual Collaborative Reasoning](https://mlsanity.com/paper/2307.00165/) (2023)
- [EulerFormer: Sequential User Behavior Modeling with Complex Vector Attention](https://mlsanity.com/paper/2403.17729/) (2024)
- [DIET: Customized Slimming for Incompatible Networks in Sequential Recommendation](https://mlsanity.com/paper/2406.08804/) (2024)
- [Forward Once for All: Structural Parameterized Adaptation for Efficient Cloud-coordinated On-device Recommendation](https://mlsanity.com/paper/2501.02837/) (2025)
- [Dynamic Causal Collaborative Filtering](https://mlsanity.com/paper/2208.11094/) (2022)
- [Deep Stable Multi-Interest Learning for Out-of-distribution Sequential Recommendation](https://mlsanity.com/paper/2304.05615/) (2023)

## Abstract (arXiv)

Learning user representations based on historical behaviors lies at the core of modern recommender systems. Recent advances in sequential recommenders have convincingly demonstrated high capability in extracting effective user representations from the given behavior sequences. Despite significant progress, we argue that solely modeling the observational behaviors sequences may end up with a brittle and unstable system due to the noisy and sparse nature of user interactions logged. In this paper, we propose to learn accurate and robust user representations, which are required to be less sensitive to (attack on) noisy behaviors and trust more on the indispensable ones, by modeling counterfactual data distribution. Specifically, given an observed behavior sequence, the proposed CauseRec framework identifies dispensable and indispensable concepts at both the fine-grained item level and the abstract interest level. CauseRec conditionally samples user concept sequences from the counterfactual data distributions by replacing dispensable and indispensable concepts within the original concept sequence. With user representations obtained from the synthesized user sequences, CauseRec performs contrastive user representation learning by contrasting the counterfactual with the observational. We conduct extensive experiments on real-world public recommendation benchmarks and justify the effectiveness of CauseRec with multi-aspects model analysis. The results demonstrate that the proposed CauseRec outperforms state-of-the-art sequential recommenders by learning accurate and robust user representations.