LettinGo enhances user profiling via diverse, adaptive profiles generated using LLMs and Direct Preference Optimization, improving recommendation accuracy and flexibility.
User profiling is pivotal for recommendation systems, as it transforms raw
user interaction data into concise and structured representations that drive
personalized recommendations. While traditional embedding-based profiles lack
interpretability and adaptability, recent advances with large language models
(LLMs) enable text-based profiles that are semantically richer and more
transparent. However, existing methods often adhere to fixed formats that limit
their ability to capture the full diversity of user behaviors. In this paper,
we introduce LettinGo, a novel framework for generating diverse and adaptive
user profiles. By leveraging the expressive power of LLMs and incorporating
direct feedback from downstream recommendation tasks, our approach avoids the
rigid constraints imposed by supervised fine-tuning (SFT). Instead, we employ
Direct Preference Optimization (DPO) to align the profile generator with
task-specific performance, ensuring that the profiles remain adaptive and
effective. LettinGo operates in three stages: (1) exploring diverse user
profiles via multiple LLMs, (2) evaluating profile quality based on their
impact in recommendation systems, and (3) aligning the profile generation
through pairwise preference data derived from task performance. Experimental
results demonstrate that our framework significantly enhances recommendation
accuracy, flexibility, and contextual awareness. This work enhances profile
generation as a key innovation for next-generation recommendation systems.