Generative Adversarial User Model for Reinforcement Learning Based Recommendation System (1812.10613v3)

Abstract: There are great interests as well as many challenges in applying reinforcement learning (RL) to recommendation systems. In this setting, an online user is the environment; neither the reward function nor the environment dynamics are clearly defined, making the application of RL challenging. In this paper, we propose a novel model-based reinforcement learning framework for recommendation systems, where we develop a generative adversarial network to imitate user behavior dynamics and learn her reward function. Using this user model as the simulation environment, we develop a novel Cascading DQN algorithm to obtain a combinatorial recommendation policy which can handle a large number of candidate items efficiently. In our experiments with real data, we show this generative adversarial user model can better explain user behavior than alternatives, and the RL policy based on this model can lead to a better long-term reward for the user and higher click rate for the system.

Overview of Generative Adversarial User Model for Reinforcement Learning Based Recommendation System

The utilization of reinforcement learning (RL) in recommendation systems is both an appealing and intricate challenge due to the undefined nature of the reward function and environment dynamics. In the context of recommendation systems, a user’s interaction history and preferences serve as the environment, making it crucial to estimate both to apply an RL framework effectively. This paper introduces an innovative model-based RL framework specifically targeting recommendation systems. The core innovation lies in employing a generative adversarial network (GAN) as a surrogate model to emulate user behavior dynamics and discern the underlying reward function of the users.

Key Concepts and Methodology

1. Generative Adversarial Network for User Modelling:
   • The proposed framework leverages GANs to model user interaction sequences as a way to simulate the environment for RL agents. This is predicated on the notion that users act based on evolving preferences which the model must understand and predict accurately.
   • The GAN framework is adept at concurrently learning user behavior dynamics and the reward functions that drive these behaviors. This is achieved through a mini-max optimization process where the GAN attempts to differentiate between real user actions and those generated by the model, leading to improved prediction capabilities.
2. Cascading Deep Q-Networks (DQN) for Efficient Policy Learning:
   • A novel Cascading DQN method is devised to manage the vast combinatorial action spaces characteristic of recommendation systems. This design enhances the RL policy’s ability to efficiently identify the optimal subset of items from a large pool of candidates to recommend, which is pivotal for maximizing user engagement.
   • The cascading architecture of the Q-networks permits a linear time complexity in optimizing actions, which is an essential feature given the large-scale nature of recommendation tasks.

Results and Findings

• User Behavior Prediction and Click Prediction:
  • Empirical results demonstrate that the GAN user model delivers superior performance in aligning with actual user behavior patterns when compared to conventional methods. The metrics used include held-out likelihood and precise click prediction, evidencing that GAN's approach to capturing user dynamics leads to more accurate predictions.
• Reinforcement Learning Policy Performance:
  • The RL policy, based on the GAN model, exhibits enhanced long-term user satisfaction and engages users more effectively, as measured by click-through rates (CTR). The RL framework's design considers intricate user interactions over extended periods, leading to significant gains in cumulative long-term reward metrics.
• Adaptability and Efficiency:
  • Notably, the model-based approach proves more sample-efficient than model-free alternatives. The ability to leverage off-policy data to refine the environment dynamics model provides a strategic advantage. The RL model can quickly adapt to new user behaviors, ensuring relevance and responsiveness.

Implications and Future Directions

The implications of this research extend into both theoretical understanding and practical application within AI-driven recommendation systems. The integration of GANs in modelling user environments adds a rich layer of adaptability and precision to RL policies. Practically, the system’s ability to consistently enhance user engagement and satisfaction through informed policy decisions represents a substantial advancement in recommendation technologies.

Future research in this domain might explore enhancing the scalability of the GAN model to accommodate even more granular user data or integrating additional user autobiographical features employing a broader set of contextual signals. Extending these models to real-world diverse recommendation scenarios remains an open challenge for researchers focused on advancing AI-influenced decision systems.

This paper provides a comprehensive framework that sets a benchmark for further exploration in refining user-adaptive recommendation systems using advanced machine learning models such as GANs and deep reinforcement learning algorithms.