Temporal User Profiling with LLMs: Balancing Short-Term and Long-Term Preferences for Recommendations
This work addresses the challenge of capturing nuanced user preference dynamics for personalized content-based recommendation, representing an incremental advancement in user profiling methods.
The paper tackled the problem of modeling user preferences in content-based recommender systems by proposing LLM-TUP, a method that leverages large language models to generate natural language representations of user histories and dynamically fuses short-term and long-term preferences, achieving substantial improvements over baselines on real-world datasets.
Accurately modeling user preferences is crucial for improving the performance of content-based recommender systems. Existing approaches often rely on simplistic user profiling methods, such as averaging or concatenating item embeddings, which fail to capture the nuanced nature of user preference dynamics, particularly the interactions between long-term and short-term preferences. In this work, we propose LLM-driven Temporal User Profiling (LLM-TUP), a novel method for user profiling that explicitly models short-term and long-term preferences by leveraging interaction timestamps and generating natural language representations of user histories using a large language model (LLM). These representations are encoded into high-dimensional embeddings using a pre-trained BERT model, and an attention mechanism is applied to dynamically fuse the short-term and long-term embeddings into a comprehensive user profile. Experimental results on real-world datasets demonstrate that LLM-TUP achieves substantial improvements over several baselines, underscoring the effectiveness of our temporally aware user-profiling approach and the use of semantically rich user profiles, generated by LLMs, for personalized content-based recommendation.