On the Memorization Behavior of LLMs in Generative Recommendation: Observations, Implications, and Training Strategies
arXiv:2606.17276v1 Announce Type: cross Abstract: Generative recommendation (GR) has emerged as a promising direction for recommender systems. Recently, large language models (LLMs) have been increasingly adopted for GR, as their rich pretrained knowledge is expected to help them generalize beyond common user behavior patterns that traditional memorization-oriented baselines can capture. However, existing LLM-based GR works largely ignore LLMs' well-known tendency to memorize, which, if present in LLMs fine-tuned for GR, would restrict their utilization of pretrained knowledge. In this work, w
The increasing adoption of large language models for generative recommendation systems is exposing a critical challenge in their inherent memorization tendencies, which is often overlooked in current research.
This research highlights a fundamental limitation in applying LLMs to recommendation systems, indicating that their expected generalization benefits might be compromised by over-memorization, thereby affecting their practical utility and development direction.
The focus for LLM-based generative recommendation systems may shift towards developing specific strategies to mitigate memorization and enhance true generalization, moving beyond reliance on pretrained knowledge alone.
- · Researchers specializing in LLM generalization
- · Developers of robust recommendation system architectures
- · Companies with diverse and high-quality data for training
- · Users benefiting from more personalized and less biased recommendations
- · Developers relying solely on LLMs' pretrained knowledge without adaptation
- · Companies with limited or biased user behavior data
- · Recommender systems that overfit to past user interactions
- · Users receiving repetitive or uninspired recommendations
Research into LLM memorization and generalization in recommendation systems will intensify.
New fine-tuning and architectural methods will emerge to counteract memorization, leading to more robust GR platforms.
The development of truly novel and personalized generative recommendation experiences will accelerate, potentially disrupting existing e-commerce and media consumption models.
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