TRU: Targeted Reverse Update for Efficient Multimodal Recommendation Unlearning

• URL: http://arxiv.org/abs/2604.02183v1
• Date: Thu, 02 Apr 2026 15:45:03 GMT
• Title: TRU: Targeted Reverse Update for Efficient Multimodal Recommendation Unlearning
• Abstract summary: Multimodal recommendation systems (MRS) jointly model user-item interaction graphs and rich item content, but this tight coupling makes user data difficult to remove once learned.<n> Approximate machine unlearning offers an efficient alternative to full retraining, yet existing methods for MRS mainly rely on a largely uniform reverse update across the model.<n>We show that this assumption is fundamentally mismatched to modern MRS: deleted-data influence is not uniformly distributed, but concentrated unevenly across textitranking behavior, textitmodality branches, and textitnetwork layers.
• Score: 10.314982171290835
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multimodal recommendation systems (MRS) jointly model user-item interaction graphs and rich item content, but this tight coupling makes user data difficult to remove once learned. Approximate machine unlearning offers an efficient alternative to full retraining, yet existing methods for MRS mainly rely on a largely uniform reverse update across the model. We show that this assumption is fundamentally mismatched to modern MRS: deleted-data influence is not uniformly distributed, but concentrated unevenly across \textit{ranking behavior}, \textit{modality branches}, and \textit{network layers}. This non-uniformity gives rise to three bottlenecks in MRS unlearning: target-item persistence in the collaborative graph, modality imbalance across feature branches, and layer-wise sensitivity in the parameter space. To address this mismatch, we propose \textbf{targeted reverse update} (TRU), a plug-and-play unlearning framework for MRS. Instead of applying a blind global reversal, TRU performs three coordinated interventions across the model hierarchy: a ranking fusion gate to suppress residual target-item influence in ranking, branch-wise modality scaling to preserve retained multimodal representations, and capacity-aware layer isolation to localize reverse updates to deletion-sensitive modules. Experiments across two representative backbones, three datasets, and three unlearning regimes show that TRU consistently achieves a better retain-forget trade-off than prior approximate baselines, while security audits further confirm deeper forgetting and behavior closer to a full retraining on the retained data.

Related papers

• A Model Merging Approach for Continual MLLM Unlearning [22.83414107472909]
  Merging for Continual Unlearning (MCU) is an approach that dynamically merges multiple one-shot unlearning adapters into a unified adapter upon receiving each new unlearning request.<n>MCU achieves superior unlearning effectiveness while preserving both retained knowledge and general multimodal utility.
  arXiv  Detail & Related papers  (2026-08-05T07:37:23Z)
• Unifying Generative Recall and Multi-Objective Ranking in a Single Decoder-Only Sequence [26.231002784432842]
  generative recall and ranking scaling share a common Transformer-based modeling philosophy.<n>We propose textbfUniR$2$, a textbfUnified decoder-only Transformer that unifies Generative textbfRecall and Multi-Objective textbfRanking.
  arXiv  Detail & Related papers  (2026-07-27T13:47:38Z)
• RelPrism: A Multi-Faceted Pre-training Framework with Self-Generated Tasks for Relational Databases [49.47033473074821]
  We propose RelPrism, a multi-faceted self-supervised learning framework for RDBs.<n>RDB tasks often require multi-faceted information across different perspectives and granularities.<n>Our experiments show that RelPrism improves ROC-AUC by 4.15% for classification and reduces MAE by 10.75% for regression over state-of-the-art baselines.
  arXiv  Detail & Related papers  (2026-05-22T05:19:58Z)
• LIMSSR: LLM-Driven Sequence-to-Score Reasoning under Training-Time Incomplete Multimodal Observations [53.20772659095155]
  This paper tackles the more challenging setting of IML under training-time incomplete observations.<n>We propose LIMSSR (LLM-Driven Incomplete Multimodal Sequence-to-Score Reasoning), a framework that reformulates this challenge as a conditional sequence reasoning task.
  arXiv  Detail & Related papers  (2026-05-01T06:11:42Z)
• Reasoning Resides in Layers: Restoring Temporal Reasoning in Video-Language Models with Layer-Selective Merging [62.24239343184817]
  Multimodal adaptation equips large language models with perceptual capabilities, but often weakens the reasoning ability inherited from language-only pretraining.<n>We propose MERIT, a training-free, task-driven model merging framework for restoring temporal reasoning in video-language models.
  arXiv  Detail & Related papers  (2026-04-13T12:41:50Z)
• TopoCurate:Modeling Interaction Topology for Tool-Use Agent Training [53.93696896939915]
  Training tool-use agents typically rely on Supervised Fine-Tuning (SFT) on successful trajectories and Reinforcement Learning (RL) on pass-rate-selected tasks.<n>We propose TopoCurate, an interaction-aware framework that projects multi-trial rollouts from the same task into a unified semantic quotient topology.<n>TopoCurate achieves consistent gains of 4.2% (SFT) and 6.9% (RL) over state-of-the-art baselines.
  arXiv  Detail & Related papers  (2026-03-02T10:38:54Z)
• Wireless Federated Multi-Task LLM Fine-Tuning via Sparse-and-Orthogonal LoRA [61.12136997430116]
  Decentralized federated learning (DFL) based on low-rank adaptation (LoRA) enables mobile devices with multi-task datasets to collaboratively fine-tune a large language model (LLM) by exchanging locally updated parameters with a subset of neighboring devices via wireless connections for knowledge integration.<n> directly aggregating parameters fine-tuned on heterogeneous datasets induces three primary issues across the DFL life-cycle: (i) catastrophic knowledge forgetting during fine-tuning process, arising from conflicting update directions caused by data heterogeneity; (ii) textitinefficient communication and convergence during model aggregation process,
  arXiv  Detail & Related papers  (2026-02-24T02:45:32Z)
• RECALL: REpresentation-aligned Catastrophic-forgetting ALLeviation via Hierarchical Model Merging [33.22889542330089]
  Internal representations in large language models (LLMs) serve as reliable proxies of learned knowledge.<n>We propose RECALL, a representation-aware model merging framework for continual learning without access to historical data.
  arXiv  Detail & Related papers  (2025-10-23T12:17:37Z)
• Empowering Large Language Model for Sequential Recommendation via Multimodal Embeddings and Semantic IDs [28.752042722391934]
  Sequential recommendation (SR) aims to capture users' dynamic interests and sequential patterns based on their historical interactions.<n>MME-SID integrates multimodal embeddings and quantized embeddings to mitigate embedding collapse.<n>Extensive experiments on three public datasets validate the superior performance of MME-SID.
  arXiv  Detail & Related papers  (2025-09-02T07:02:29Z)
• Rethinking Layer-wise Model Merging through Chain of Merges [21.26982153528304]
  Chain of Merges (CoM) is a layer-wise merging procedure that sequentially merges weights across layers while sequentially updating activation statistics.<n> Experiments on standard benchmarks demonstrate that CoM achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2025-08-29T08:44:47Z)
• I$^3$-MRec: Invariant Learning with Information Bottleneck for Incomplete Modality Recommendation [56.55935146424585]
  We introduce textbfI$3$-MRec, which learns with textbfInformation bottleneck principle for textbfIncomplete textbfModality textbfRecommendation.<n>By treating each modality as a distinct semantic environment, I$3$-MRec employs invariant risk minimization (IRM) to learn preference-oriented representations.<n>I$3$-MRec consistently outperforms existing state-of-the-art MRS methods across various modality-missing scenarios
  arXiv  Detail & Related papers  (2025-08-06T09:29:50Z)
• DLF: Enhancing Explicit-Implicit Interaction via Dynamic Low-Order-Aware Fusion for CTR Prediction [71.41414150295702]
  We propose a novel framework, Dynamic Low-Order-Aware Fusion (DLF), for modeling click-through rate (CTR) prediction.<n>RLI preserves low-order signals while mitigating redundancy from residual connections, and NAF dynamically integrates explicit and implicit representations at each layer, enhancing information sharing.<n>Experiments on public datasets demonstrate that DLF achieves state-of-the-art performance in CTR prediction, addressing key limitations of existing models.
  arXiv  Detail & Related papers  (2025-05-25T15:05:00Z)
• Towards Generalizable Trajectory Prediction Using Dual-Level Representation Learning And Adaptive Prompting [107.4034346788744]
  Existing vehicle trajectory prediction models struggle with generalizability, prediction uncertainties, and handling complex interactions.<n>We propose Perceiver with Register queries (PerReg+), a novel trajectory prediction framework that introduces: (1) Dual-Level Representation Learning via Self-Distillation (SD) and Masked Reconstruction (MR), capturing global context and fine-grained details; (2) Enhanced Multimodality using register-based queries and pretraining, eliminating the need for clustering and suppression; and (3) Adaptive Prompt Tuning during fine-tuning, freezing the main architecture and optimizing a small number of prompts for efficient adaptation.
  arXiv  Detail & Related papers  (2025-01-08T20:11:09Z)
• CLIPErase: Efficient Unlearning of Visual-Textual Associations in CLIP [57.49519639951552]
  We introduce CLIPErase, a novel approach that disentangles and selectively forgets both visual and textual associations.<n>Experiments on the CIFAR-100 and Flickr30K datasets demonstrate that CLIPErase effectively forgets designated associations in zero-shot tasks for multimodal samples.
  arXiv  Detail & Related papers  (2024-10-30T17:51:31Z)
• Efficient Multimodal Transformer with Dual-Level Feature Restoration for Robust Multimodal Sentiment Analysis [47.29528724322795]
  Multimodal Sentiment Analysis (MSA) has attracted increasing attention recently. Despite significant progress, there are still two major challenges on the way towards robust MSA. We propose a generic and unified framework to address them, named Efficient Multimodal Transformer with Dual-Level Feature Restoration (EMT-DLFR)
  arXiv  Detail & Related papers  (2022-08-16T08:02:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.