Logits-Level Balanced Machine Unlearning for LLM-Based Recommendation System

As recommendation systems have become foundational to digital platforms, the integration of large language models (LLMs) into these systems offers transformative potential. In LLM-based recommendation (LLMRec) systems, LLMs excel at capturing nuanced user pReferences, greatly enhancing personalization and recommendation accuracy. However, LLMRec systems face data governance issues, such as data privacy, outdated data, poisoned data, and copyrighted data. All these data government scenarios require the removal of data and its impact from large models like LLMRec. This poses significant challenges as existing solutions struggle to accurately delete target data from such complex systems. Motivated by this, we propose an LLMRec unlearning system based on adapter-driven logits modification in this work. This mechanism addresses the unique characteristics of LLMRec's complex parameters and network structure, enabling precise and effective unlearning while maintaining recommendation performance. Our approach offers several key advantages: 1) using adapters reduces training costs during the unlearning process; 2) a knowledge distillation (KD)-powered logits modification mechanism ensures that the model can still engage in effective reasoning after unlearning, leveraging its existing knowledge logic to maintain high-quality recommendations; and 3) an additional adapter-based general knowledge retention module is incorporated to preserve the core language capabilities of the LLMRec system, ensuring that its foundational performance remains intact. Extensive experimental results show the effectiveness and efficiency of the proposed system. The code is released at: https://anonymous.4open.science/r/llmrecunlearning-1CB3.