Deep Reinforcement Learning for List-wise Recommendations (1801.00209v3)

Abstract: Recommender systems play a crucial role in mitigating the problem of information overload by suggesting users' personalized items or services. The vast majority of traditional recommender systems consider the recommendation procedure as a static process and make recommendations following a fixed strategy. In this paper, we propose a novel recommender system with the capability of continuously improving its strategies during the interactions with users. We model the sequential interactions between users and a recommender system as a Markov Decision Process (MDP) and leverage Reinforcement Learning (RL) to automatically learn the optimal strategies via recommending trial-and-error items and receiving reinforcements of these items from users' feedbacks. In particular, we introduce an online user-agent interacting environment simulator, which can pre-train and evaluate model parameters offline before applying the model online. Moreover, we validate the importance of list-wise recommendations during the interactions between users and agent, and develop a novel approach to incorporate them into the proposed framework LIRD for list-wide recommendations. The experimental results based on a real-world e-commerce dataset demonstrate the effectiveness of the proposed framework.

Deep Reinforcement Learning for List-wise Recommendations

The paper presents a novel approach to recommender systems by leveraging Deep Reinforcement Learning (DRL) to develop a model, termed as LIRD, that focuses on list-wise recommendations. Traditional recommender systems are often limited by static processes and prioritize short-term rewards, usually leading to ineffective personalization as user preferences evolve. The authors propose addressing these limitations using a DRL framework by modeling interactions between users and recommendation agents as a Markov Decision Process (MDP). This allows the system to continuously adapt and optimize its strategies to maximize long-term cumulative rewards.

Approach and Methodology

The paper introduces a reinforcement learning framework where recommendations are made as sequences of decisions, optimizing both immediate and delayed rewards. Two key components of the proposed system are:

1. Actor-Critic Architecture: This architecture facilitates the separation of policy learning (actor) from value learning (critic), allowing the system to handle large action spaces efficiently. The actor generates parameters for a state-specific scoring function, while the critic evaluates the action-value function to ensure the proposed actions align with the desired outcomes. This separation mitigates the computational overhead typically associated with estimating Q-values directly for large item spaces.
2. Online Environment Simulator: The authors implement an offline user-agent interaction simulator to pre-train and evaluate recommendation strategies before deploying them online. This simulator uses users' historical records to predict their feedback on recommended items, addressing the challenge of obtaining timely rewards in real-world applications.

Evaluation and Results

The effectiveness of the LIRD framework is demonstrated through experiments on a real-world e-commerce dataset. The experimental results illustrate the superiority of list-wise recommendations over traditional item-wise approaches. Specifically, metrics such as Mean Average Precision (MAP) and Normalized Discounted Cumulative Gain (NDCG) show improvement in recommendation accuracy, especially in longer session interactions. The framework's ability to balance short-term and long-term reward optimization further distinguishes it from baselines like Collaborative Filtering (CF) and Recurrent Neural Networks (RNN).

Implications and Speculation

This research implies potential advancements in recommendation systems where dynamic user interactions are crucial. By modeling recommendations as an MDP and employing DRL, systems can better capture evolving user preferences and provide more personalized experiences. The paper opens pathways for future research in areas such as integrating temporal dynamics into recommendation strategies, exploring multi-faceted user interactions like item bundling, and expanding applications beyond e-commerce to areas like content streaming and social media engagement.

In conclusion, the paper offers significant contributions to recommendation system research by harnessing DRL for list-wise strategies, which could lead to more adaptable and effective personalization in various domains. As DRL techniques continue to evolve, their integration into recommendation systems promises a step towards more intelligent and user-centric solutions.