# Recommendation Unlearning via Influence Function

arXiv: https://arxiv.org/abs/2307.02147 · HTML: https://mlsanity.com/paper/2307.02147/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jul 2023.

## Built on by (most cited first, 22 of 22)

- [Towards Efficient and Effective Unlearning of Large Language Models for Recommendation](https://mlsanity.com/paper/2403.03536/) (2024)
- [Dynamic Graph Unlearning: A General and Efficient Post-Processing Method via Gradient Transformation](https://mlsanity.com/paper/2405.14407/) (2024)
- [A Survey of Graph Unlearning](https://mlsanity.com/paper/2310.02164/) (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)
- [Recommendation Unlearning via Matrix Correction](https://mlsanity.com/paper/2307.15960/) (2023)
- [CURE4Rec: A Benchmark for Recommendation Unlearning with Deeper Influence](https://mlsanity.com/paper/2408.14393/) (2024)
- [IGD: Token Decisiveness Modeling via Information Gain in LLMs for Personalized Recommendation](https://mlsanity.com/paper/2506.13229/) (2025)
- [How Secure is Forgetting? Linking Machine Unlearning to Machine Learning Attacks](https://mlsanity.com/paper/2503.20257/) (2025)
- [Unlearning Inversion Attacks for Graph Neural Networks](https://mlsanity.com/paper/2506.00808/) (2025)
- [Influence Functions for Edge Edits in Non-Convex Graph Neural Networks](https://mlsanity.com/paper/2506.04694/) (2025)
- [Exploring Incremental Unlearning: Techniques, Challenges, and Future Directions](https://mlsanity.com/paper/2502.16708/) (2025)
- [Train Once, Forget Precisely: Anchored Optimization for Efficient Post-Hoc Unlearning](https://mlsanity.com/paper/2506.14515/) (2025)
- [Towards Independence Criterion in Machine Unlearning of Features and Labels](https://mlsanity.com/paper/2403.08124/) (2024)
- [TAIJI: MCP-based Multi-Modal Data Analytics on Data Lakes](https://mlsanity.com/paper/2505.11270/) (2025)
- [Customized Retrieval-Augmented Generation with LLM for Debiasing Recommendation Unlearning](https://mlsanity.com/paper/2511.05494/) (2025)
- [Soft Weighted Machine Unlearning](https://mlsanity.com/paper/2505.18783/) (2025)
- [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)
- [TRU: Targeted Reverse Update for Efficient Multimodal Recommendation Unlearning](https://mlsanity.com/paper/2604.02183/) (2026)
- [Obliviate: Efficient Unlearning in Recommender Systems](https://mlsanity.com/paper/2607.22665/) (2026)
- [OSR: Output Space Redistribution for Adaptive Label Removal in Classification Models](https://mlsanity.com/paper/2609.03972/) (2026)

## Abstract (arXiv)

Recommendation unlearning is an emerging task to serve users for erasing unusable data (e.g., some historical behaviors) from a well-trained recommender model. Existing methods process unlearning requests by fully or partially retraining the model after removing the unusable data. However, these methods are impractical due to the high computation cost of full retraining and the highly possible performance damage of partial training. In this light, a desired recommendation unlearning method should obtain a similar model as full retraining in a more efficient manner, i.e., achieving complete, efficient and harmless unlearning. In this work, we propose a new Influence Function-based Recommendation Unlearning (IFRU) framework, which efficiently updates the model without retraining by estimating the influence of the unusable data on the model via the influence function. In the light that recent recommender models use historical data for both the constructions of the optimization loss and the computational graph (e.g., neighborhood aggregation), IFRU jointly estimates the direct influence of unusable data on optimization loss and the spillover influence on the computational graph to pursue complete unlearning. Furthermore, we propose an importance-based pruning algorithm to reduce the cost of the influence function. IFRU is harmless and applicable to mainstream differentiable models. Extensive experiments demonstrate that IFRU achieves more than 250 times acceleration compared to retraining-based methods with recommendation performance comparable to full retraining. Codes are avaiable at https://github.com/baiyimeng/IFRU.