TRU: Targeted Reverse Update for Efficient Multimodal Recommendation Unlearning

arXiv:2604.02183 · cs.AI · Submitted 2026-04-02 · Read on arXiv

cs.AI

Submitted: 2026-04-02

Updated: 2026-09-01

Project page: https://amazon-reviews-2023.github.io

Terminology

• Machine Unlearning — 11× in this paper · explained in 2 episodes
• Trace — 7× in this paper · explained in 2 episodes
• Multimodal Recommendation Systems — 5× in this paper
• Alignment — 4× in this paper · explained in 1 episode
• Approximate Unlearning — 4× in this paper · explained in 1 episode
• Transformer — 4× in this paper · explained in 2 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Accuracy — 3× in this paper · explained in 1 episode
• Benchmark — 3× in this paper · explained in 2 episodes
• Decoupling — 3× in this paper · explained in 2 episodes
• Sparsity — 3× in this paper · explained in 4 episodes
• Verification — 3× in this paper · explained in 2 episodes
• Adam — 2× in this paper · explained in 2 episodes
• Attack Success Rate — 2× in this paper
• Membership Inference Attacks — 2× in this paper
• Regularization — 2× in this paper · explained in 3 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Adaptive Selection — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Attack Success Rate (ASR) — 1× in this paper · explained in 2 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• California Consumer Privacy Act — 1× in this paper
• Centered Kernel Alignment (CKA) — 1× in this paper · explained in 2 episodes
• Clustering — 1× in this paper · explained in 2 episodes
• Continual Learning — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Error Decomposition — 1× in this paper · explained in 3 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• GNNs — 1× in this paper · explained in 2 episodes
• Personalization — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes

Sources

• GNNDelete: A General Strategy for Unlearning in Graph Neural Networks
• Reliability of CKA as a Similarity Measure in Deep Learning
• Bridging Language and Items for Retrieval and Recommendation: Benchmarking LLMs as Semantic Encoders
• A Survey of Foundation Model-Powered Recommender Systems: From Feature-Based, Generative to Agentic Paradigms
• Adam: A Method for Stochastic Optimization
• BPR: Bayesian Personalized Ranking from Implicit Feedback
• Multi-Modal Recommendation Unlearning for Legal, Licensing, and Modality Constraints
• A Survey on Multimodal Recommender Systems: Recent Advances and Future Directions
• Scalable and Certifiable Graph Unlearning: Overcoming the Approximation Error Barrier
• A Comprehensive Survey on Multimodal Recommender Systems: Taxonomy, Evaluation, and Future Directions
• A Survey of Real-World Recommender Systems: Challenges, Constraints, and Industrial Perspectives

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