Enhancing Sequential Recommender Systems with Robust Reinforcement Learning Objectives

Core Concepts

Integrating contrastive learning and conservative Q-learning objectives can enhance the stability and performance of reinforcement learning-based sequential recommender systems.

Abstract

The content discusses the challenges in applying reinforcement learning (RL) to sequential recommender systems, such as off-policy training, large action spaces, and data scarcity. It proposes a framework that combines two key enhancements to address these issues: Contrastive Learning: The authors apply a contrastive learning objective to learn effective representations of states and actions, which can improve the learning potential of the Q-function. Conservative Q-Learning (CQL): The authors introduce CQL to prevent the overestimation of Q-values, which can lead to sub-optimal policies. CQL aims to learn a conservative value function that minimizes an upper bound on the expected value of a policy. The authors conduct extensive experiments on several real-world datasets and demonstrate that their proposed approach, SASRec-CCQL, outperforms various baseline methods in terms of recommendation performance and training stability. They also provide insights into the impact of negative sampling strategies and the effects of short-horizon versus long-horizon reward estimations.

Stats

The RetailRocket dataset contains 1,176,680 clicks and 57,269 purchases over 70,852 items. The RC15 dataset is based on the RecSys Challenge 2015 and consists of sequences of clicks and purchases. The Yelp dataset contains user reviews and interactions, which are interpreted as rewards. The MovieLens-1M dataset is a large collection of movie ratings used as a non-RL-based baseline.

Quotes

"By incorporating RL into the recommendation process, the system can actively adapt to changing user preferences and item catalogs, maximizing long-term user satisfaction rather than merely focusing on immediate rewards." "Our extensive experimentation across multiple datasets demonstrates our method not only enhances the precision of recommendations in comparison to the baseline, but also adds further stability to the training process."

Key Insights Distilled From

Robust Reinforcement Learning Objectives for Sequential Recommender Systems

by Melissa Mozi... at arxiv.org 04-19-2024

https://arxiv.org/pdf/2305.18820.pdf

Deeper Inquiries

How can the proposed framework be extended to handle dynamic user preferences and item catalogs in real-time recommendation scenarios

The proposed framework can be extended to handle dynamic user preferences and item catalogs in real-time recommendation scenarios by incorporating techniques for continuous learning and adaptation. One approach could involve implementing online reinforcement learning algorithms that can update the model in real-time based on new user interactions and feedback. This would allow the system to adapt to changing user preferences and item availability dynamically. Additionally, the integration of contextual bandit algorithms could help in making personalized recommendations in real-time by considering the current context of the user. Another way to enhance the framework for real-time scenarios is to incorporate techniques for exploring and exploiting user feedback efficiently. This could involve using multi-armed bandit algorithms to balance between exploring new recommendations and exploiting known preferences. By continuously learning from user interactions and adjusting the recommendation strategy accordingly, the system can provide more relevant and up-to-date recommendations to users.

What are the potential drawbacks or limitations of the conservative Q-learning approach, and how could they be addressed in future research

The conservative Q-learning approach, while effective in addressing overestimation bias in Q-learning, has some potential drawbacks and limitations. One limitation is the risk of underestimating Q-values, leading to overly cautious policies that may not always align with the optimal strategy. This can result in suboptimal performance and reduced exploration of the action space. To address these limitations, future research could focus on developing adaptive conservative Q-learning algorithms that dynamically adjust the level of conservatism based on the learning progress and the characteristics of the environment. By incorporating mechanisms for balancing between exploration and exploitation, the algorithm can adapt its level of conservatism to optimize performance while mitigating the risk of underestimation. Additionally, exploring ensemble methods that combine conservative Q-learning with other reinforcement learning approaches could help in leveraging the strengths of different algorithms to achieve more robust and efficient learning. By integrating diverse strategies, the system can benefit from a more comprehensive and balanced approach to reinforcement learning.

How could the integration of contrastive learning and reinforcement learning be applied to other domains beyond recommender systems, such as dialogue systems or autonomous decision-making agents

The integration of contrastive learning and reinforcement learning can be applied to other domains beyond recommender systems, such as dialogue systems or autonomous decision-making agents, to enhance learning and representation capabilities. In dialogue systems, the combination of contrastive learning can help in learning more informative and contextually relevant representations of dialogues, improving the system's understanding of user intents and responses. For autonomous decision-making agents, the integration of contrastive learning with reinforcement learning can aid in learning robust and generalizable policies by capturing meaningful relationships between states and actions. This approach can enhance the agent's ability to make informed decisions in complex and dynamic environments, leading to more efficient and adaptive behavior. Furthermore, in natural language processing tasks, the fusion of contrastive learning with reinforcement learning can assist in learning better semantic representations of text data, enabling more accurate and context-aware language understanding models. By leveraging the benefits of both techniques, these domains can benefit from improved learning efficiency, performance, and generalization capabilities.