# Making Recommender Systems Forget: Learning and Unlearning for Erasable Recommendation

arXiv: https://arxiv.org/abs/2203.11491 · HTML: https://mlsanity.com/paper/2203.11491/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Placed in Membership inference attacks, one of 12 directions in LLM safety and security.
- Builds on 19 of its 55 references that are in the map.
- First posted on arXiv 22 Mar 2022.

## Placed in

- LLM safety and security / [Membership inference attacks](https://mlsanity.com/safety/membership-inference-attacks/)

## Builds on (most cited first, 19 of 19)

- [Understanding Black-box Predictions via Influence Functions](https://mlsanity.com/paper/1703.04730/) (2017)
- [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://mlsanity.com/paper/2002.02126/) (2020)
- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [Machine Unlearning](https://mlsanity.com/paper/1912.03817/) (2019)
- [Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering](https://mlsanity.com/paper/1602.01585/) (2016)
- [Making AI Forget You: Data Deletion in Machine Learning](https://mlsanity.com/paper/1907.05012/) (2019)
- [Remember What You Want to Forget: Algorithms for Machine Unlearning](https://mlsanity.com/paper/2103.03279/) (2021)
- [Amnesiac Machine Learning](https://mlsanity.com/paper/2010.10981/) (2020)
- [On The Power of Curriculum Learning in Training Deep Networks](https://mlsanity.com/paper/1904.03626/) (2019)
- [On the Accuracy of Influence Functions for Measuring Group Effects](https://mlsanity.com/paper/1905.13289/) (2019)
- [Graph Unlearning](https://mlsanity.com/paper/2103.14991/) (2021)
- [Neural Collaborative Filtering vs. Matrix Factorization Revisited](https://mlsanity.com/paper/2005.09683/) (2020)
- [Higher-order organization of complex networks](https://mlsanity.com/paper/1612.08447/) (2016)
- [Machine Unlearning: Linear Filtration for Logit-based Classifiers](https://mlsanity.com/paper/2002.02730/) (2020)
- [Data Poisoning Attacks on Factorization-Based Collaborative Filtering](https://mlsanity.com/paper/1608.08182/) (2016)
- [Recommendation Unlearning](https://mlsanity.com/paper/2201.06820/) (2022)
- [PUMA: Performance Unchanged Model Augmentation for Training Data Removal](https://mlsanity.com/paper/2203.00846/) (2022)
- [Random Walks on Hypergraphs with Edge-Dependent Vertex Weights](https://mlsanity.com/paper/1905.08287/) (2019)
- [Rethinking Neural vs. Matrix-Factorization Collaborative Filtering: the Theoretical Perspectives](https://mlsanity.com/paper/2110.12141/) (2021)

## Built on by (most cited first, 29 of 29)

- [MUSE: Machine Unlearning Six-Way Evaluation for Language Models](https://mlsanity.com/paper/2407.06460/) (2024)
- [Rethinking Machine Unlearning for Large Language Models](https://mlsanity.com/paper/2402.08787/) (2024)
- [A Survey on Federated Unlearning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/2310.20448/) (2023)
- [Exploring the Landscape of Machine Unlearning: A Comprehensive Survey and Taxonomy](https://mlsanity.com/paper/2305.06360/) (2023)
- [GNNDelete: A General Strategy for Unlearning in Graph Neural Networks](https://mlsanity.com/paper/2302.13406/) (2023)
- [A Review on Machine Unlearning](https://mlsanity.com/paper/2411.11315/) (2024)
- [MultiDelete for Multimodal Machine Unlearning](https://mlsanity.com/paper/2311.12047/) (2023)
- [Machine Unlearning: A Survey](https://mlsanity.com/paper/2306.03558/) (2023)
- [MEOW: MEMOry Supervised LLM Unlearning Via Inverted Facts](https://mlsanity.com/paper/2409.11844/) (2024)
- [Towards Efficient and Effective Unlearning of Large Language Models for Recommendation](https://mlsanity.com/paper/2403.03536/) (2024)
- [Making Users Indistinguishable: Attribute-wise Unlearning in Recommender Systems](https://mlsanity.com/paper/2310.05847/) (2023)
- [BLUR: A Benchmark for LLM Unlearning Robust to Forget-Retain Overlap](https://mlsanity.com/paper/2506.15699/) (2025)
- [Exact Unlearning of Finetuning Data via Model Merging at Scale](https://mlsanity.com/paper/2504.04626/) (2025)
- [Netflix and Forget: Efficient and Exact Machine Unlearning from Bi-linear Recommendations](https://mlsanity.com/paper/2302.06676/) (2023)
- [Machine Unlearning for Recommendation Systems: An Insight](https://mlsanity.com/paper/2401.10942/) (2024)
- [A Survey on Recommendation Unlearning: Fundamentals, Taxonomy, Evaluation, and Open Questions](https://mlsanity.com/paper/2412.12836/) (2024)
- [Class-wise Federated Unlearning: Harnessing Active Forgetting with Teacher-Student Memory Generation](https://mlsanity.com/paper/2307.03363/) (2023)
- [Recommendation Unlearning via Matrix Correction](https://mlsanity.com/paper/2307.15960/) (2023)
- [Multi-Modal Recommendation Unlearning for Legal, Licensing, and Modality Constraints](https://mlsanity.com/paper/2405.15328/) (2024)
- [Controllable Unlearning for Image-to-Image Generative Models via varepsilon-Constrained Optimization](https://mlsanity.com/paper/2408.01689/) (2024)
- [On the Effectiveness of Unlearning in Session-Based Recommendation](https://mlsanity.com/paper/2312.14447/) (2023)
- [Membership Inference Attacks on Recommender System: A Survey](https://mlsanity.com/paper/2509.11080/) (2025)
- [Privacy in LLM-based Recommendation: Recent Advances and Future Directions](https://mlsanity.com/paper/2406.01363/) (2024)
- [Revisiting Privacy Leakage in Machine Unlearning: Membership Inference Beyond the Forgotten Set](https://mlsanity.com/paper/2605.01129/) (2026)
- [Customized Retrieval-Augmented Generation with LLM for Debiasing Recommendation Unlearning](https://mlsanity.com/paper/2511.05494/) (2025)
- [U-CAN: Utility-Aware Contrastive Attenuation for Efficient Unlearning in Generative Recommendation](https://mlsanity.com/paper/2602.23400/) (2026)
- [Recommendation with User Active Disclosing Willingness](https://mlsanity.com/paper/2211.01155/) (2022)
- [Pre-training for Recommendation Unlearning](https://mlsanity.com/paper/2505.22649/) (2025)
- [LEGO: A Lightweight and Efficient Multiple-Attribute Unlearning Framework for Recommender Systems](https://mlsanity.com/paper/2510.20327/) (2025)

## Abstract (arXiv)

Privacy laws and regulations enforce data-driven systems, e.g., recommender systems, to erase the data that concern individuals. As machine learning models potentially memorize the training data, data erasure should also unlearn the data lineage in models, which raises increasing interest in the problem of Machine Unlearning (MU). However, existing MU methods cannot be directly applied into recommendation. The basic idea of most recommender systems is collaborative filtering, but existing MU methods ignore the collaborative information across users and items. In this paper, we propose a general erasable recommendation framework, namely LASER, which consists of Group module and SeqTrain module. Firstly, Group module partitions users into balanced groups based on their similarity of collaborative embedding learned via hypergraph. Then SeqTrain module trains the model sequentially on all groups with curriculum learning. Both theoretical analysis and experiments on two real-world datasets demonstrate that LASER can not only achieve efficient unlearning, but also outperform the state-of-the-art unlearning framework in terms of model utility.