Reinforcement Learning-based Recommender Systems with Large Language Models for State Reward and Action Modeling (2403.16948v1)

Abstract: Reinforcement Learning (RL)-based recommender systems have demonstrated promising performance in meeting user expectations by learning to make accurate next-item recommendations from historical user-item interactions. However, existing offline RL-based sequential recommendation methods face the challenge of obtaining effective user feedback from the environment. Effectively modeling the user state and shaping an appropriate reward for recommendation remains a challenge. In this paper, we leverage language understanding capabilities and adapt LLMs as an environment (LE) to enhance RL-based recommenders. The LE is learned from a subset of user-item interaction data, thus reducing the need for large training data, and can synthesise user feedback for offline data by: (i) acting as a state model that produces high quality states that enrich the user representation, and (ii) functioning as a reward model to accurately capture nuanced user preferences on actions. Moreover, the LE allows to generate positive actions that augment the limited offline training data. We propose a LE Augmentation (LEA) method to further improve recommendation performance by optimising jointly the supervised component and the RL policy, using the augmented actions and historical user signals. We use LEA, the state and reward models in conjunction with state-of-the-art RL recommenders and report experimental results on two publicly available datasets.

Reinforcement Learning-based Recommender Systems with LLMs for State Reward and Action Modeling

The research paper, "Reinforcement Learning-based Recommender Systems with LLMs for State Reward and Action Modeling," presents a novel approach to enhancing Reinforcement Learning (RL)-based recommender systems (RS) through the integration of capabilities from LLMs. The authors address a significant challenge in RL-based sequential recommendation: the difficulty in obtaining effective user feedback and accurately modeling user states and rewards using historical user-item interaction data.

Methodology and Key Contributions

The authors propose leveraging LLMs to create an environment (LE) that can provide higher-quality user state representations, more accurate reward models, and generate augmented positive actions to improve the performance of RL-based recommender systems. Key contributions of the paper are as follows:

1. LLM as Environment (LE): The paper introduces the concept of using LLMs to simulate user environments, thereby generating user feedback in the form of state representations and rewards. The LLM is fine-tuned using a small subset of user-item interaction data to reduce the need for extensive training data.
2. State and Reward Modeling: The LE comprises a state model (SM) and a reward model (RM). The SM enriches the user representation by generating high-quality states from historical interactions, while the RM captures nuanced user preferences and assigns accurate rewards to actions.
3. LE Augmentation (LEA): The authors propose an augmentation strategy to enhance offline training data for the RL-based recommender system. The LE is used to generate potential positive feedback, which is then employed to augment both the supervised learning component and the RL agent’s training.
4. Experimental Validation: The proposed methodologies were evaluated using two publicly available datasets, demonstrating significant improvements over state-of-the-art RL-based sequential recommendation models.

Experimental Results

The experimental results highlight several strong points:

• Performance Gains: The integration of LE into RL-based recommender systems, specifically the use of LEA to incorporate augmented positive actions, resulted in notable improvements in recommendation accuracy. LEA outperformed both standard supervised learning models and existing RL-based models across various metrics.
• Scalability and Efficiency: The use of LLMs to model user environments was shown to be efficient, as the fine-tuning process utilized only a small fraction of the original data. This suggests that the approach is scalable and can be adapted to larger datasets with minimal computational overhead.

Practical and Theoretical Implications

The application of LLMs as an integral component of RL-based recommender systems holds significant potential for advancing the state of recommendation technology. By leveraging the language understanding and generative abilities of LLMs, the proposed LE framework offers a more nuanced and accurate reflection of user preferences, which directly translates to better recommendation quality.

On a practical level, the proposed method is highly deployable, as it does not impose additional computational burdens during the inference stage. This aspect is crucial for real-world applicability, where inference speed and efficiency are critical.

Future Directions

Future research could explore several avenues:

• Enhanced Reward Strategies: Developing more sophisticated reward models that can cater to a wider range of user behaviors and preferences could further enhance the performance of RL-based recommenders.
• Incorporation of Additional User Data: Integrating more diverse types of user behavior data (e.g., user reviews, social media interactions) into the LLM training process could lead to even more accurate state representations and rewards.
• Advanced Fine-Tuning Techniques: Investigating alternative and more advanced fine-tuning methods for LLMs, such as zero-shot learning or few-shot learning, could provide more robust and versatile models for state and reward generation.

In conclusion, this paper demonstrates how the intersection of RL-based recommender systems and LLMs can yield substantial improvements in recommendation quality. The novel approach to user state and reward modeling, combined with an efficient augmentation methodology, sets a promising direction for future advancements in AI-driven recommender systems.