RL4RS: A Real-World Dataset for Reinforcement Learning based Recommender System (2110.11073v5)

Abstract: Reinforcement learning based recommender systems (RL-based RS) aim at learning a good policy from a batch of collected data, by casting recommendations to multi-step decision-making tasks. However, current RL-based RS research commonly has a large reality gap. In this paper, we introduce the first open-source real-world dataset, RL4RS, hoping to replace the artificial datasets and semi-simulated RS datasets previous studies used due to the resource limitation of the RL-based RS domain. Unlike academic RL research, RL-based RS suffers from the difficulties of being well-validated before deployment. We attempt to propose a new systematic evaluation framework, including evaluation of environment simulation, evaluation on environments, counterfactual policy evaluation, and evaluation on environments built from test set. In summary, the RL4RS (Reinforcement Learning for Recommender Systems), a new resource with special concerns on the reality gaps, contains two real-world datasets, data understanding tools, tuned simulation environments, related advanced RL baselines, batch RL baselines, and counterfactual policy evaluation algorithms. The RL4RS suite can be found at https://github.com/fuxiAIlab/RL4RS. In addition to the RL-based recommender systems, we expect the resource to contribute to research in applied reinforcement learning.

• The paper introduces the first open-source real-world dataset for RL-based recommender systems, overcoming the limitations of simulated environments.
• It presents a comprehensive evaluation framework incorporating online simulation and offline counterfactual policy evaluation techniques.
• Benchmark results show that RL models trained on RL4RS outperform traditional supervised methods, promising enhanced recommendation performance.

Overview of RL4RS: A Real-World Dataset for Reinforcement Learning-Based Recommender Systems

The research paper titled "RL4RS: A Real-World Dataset for Reinforcement Learning-based Recommender Systems" presents a significant contribution to the field of reinforcement learning (RL) applied to recommender systems (RS). The authors from Fuxi AI Lab, NetEase Inc. focus on addressing a critical gap in RL-based RS research, providing the first open-source real-world dataset, RL4RS, alongside a systematic evaluation framework. This dataset introduces a novel opportunity to replace previously used artificial and semi-simulated datasets, enhancing the capacity for realistic policy evaluation and learning.

Dataset Composition and Motivation

The RL4RS dataset is meticulously constructed to address the particular needs of RL-based RS, which has traditionally suffered from a disconnect between simulated research environments and real-world applicability. The dataset includes two real-world datasets with a focus on novel recommendation scenarios. This includes slate and sequential slate recommendations, providing a basis for addressing complex decision-making scenarios characteristic of modern ecommerce environments. The ability to model real-world user interactions and feedback with detailed logged data marks a substantial step forward in dataset quality for RL-based RS research.

Evaluation Framework

Recognizing the challenges in assessing RL-based RS models, the authors propose a comprehensive evaluation framework. This framework includes environment simulation evaluation, online policy evaluation through simulation environments, and offline policy evaluation using counterfactual policy evaluation (CPE) techniques. This multidimensional evaluation strategy aims to provide unbiased assessment methods that are crucial for validating learned policies before real-world deployment—a task that is notoriously expensive and risky without reliable validation methods.

Strong Numerical Results and Algorithms

The paper discusses the benchmark results of several state-of-the-art RL algorithms within this framework. Notably, it includes both model-free and model-based approaches, such as DQN, PPO, and batch RL methods like BCQ and CQL. The empirical results underscore that RL models, when trained and evaluated on the RL4RS datasets, outperform traditional SL-based approaches, thereby validating the dataset’s usefulness for advancing RL-based RS strategies.

Practical Implications and Future Directions

Practically, RL4RS offers a new standard for RL-based RS development and assessment, potentially accelerating the deployment of more effective recommendation systems across diverse industries. The availability of industrial-scale logged data enhances the potential for RL practitioners to design, train, and evaluate models that closer mimic real-world user systems. The introduction of evaluation frameworks encourages a shift towards more rigorous and replicable research practices.

Theoretically, RL4RS aids in understanding the long-term strategic planning in recommendation tasks, highlighting the benefits of evaluating sequences and actions considering future rewards. This opens up research on long-term user engagement and conversion strategies, areas that are traditionally overlooked by myopic models.

Future research pathways could benefit from RL4RS through exploring sophisticated simulation environment models, improving batch RL methods in the RS context, and refining evaluation metrics. The potential discovery of novel RL algorithms tailored to RS's unique challenges is substantial, driven by the depth and accessibility of the RL4RS dataset.

In conclusion, the RL4RS dataset establishes a foundational benchmark for advancing research in RL-based recommendation systems, offering robust tools and methodologies essential for bridging the gap between theoretical model development and practical deployment.