  Intent-aware session recommendation (ISR) is pivotal in discerning user
intents within sessions for precise predictions. Traditional approaches,
however, face limitations due to their presumption of a uniform number of
intents across all sessions. This assumption overlooks the dynamic nature of
user sessions, where the number and type of intentions can significantly vary.
In addition, these methods typically operate in latent spaces, thus hinder the
model's transparency.Addressing these challenges, we introduce a novel ISR
approach, utilizing the advanced reasoning capabilities of large language
models (LLMs). First, this approach begins by generating an initial prompt that
guides LLMs to predict the next item in a session, based on the varied intents
manifested in user sessions. Then, to refine this process, we introduce an
innovative prompt optimization mechanism that iteratively self-reflects and
adjusts prompts. Furthermore, our prompt selection module, built upon the LLMs'
broad adaptability, swiftly selects the most optimized prompts across diverse
domains. This new paradigm empowers LLMs to discern diverse user intents at a
semantic level, leading to more accurate and interpretable session
recommendations. Our extensive experiments on three real-world datasets
demonstrate the effectiveness of our method, marking a significant advancement
in ISR systems.
