BLADE uses Bayesian list-wise alignment with dynamic estimation to create a self-evolving target that overcomes limitations of static references in LLM-based recommendation, yielding sustained gains in ranking and complex metrics.
Deep Reinforcement Learning for List-wise Recommendations
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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.
representative citing papers
A modified pointer network trained with actor-critic DRL and Equal Size K-Means clustering is applied to combinatorial keyword recommendation in sponsored search, reporting offline and online gains.
Reinforcement learning policies for time-constrained slate recommendations improve engagement over contextual bandits in e-commerce settings.
citing papers explorer
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Beyond Static Best-of-N: Bayesian List-wise Alignment for LLM-based Recommendation
BLADE uses Bayesian list-wise alignment with dynamic estimation to create a self-evolving target that overcomes limitations of static references in LLM-based recommendation, yielding sustained gains in ranking and complex metrics.
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Combinatorial Keyword Recommendations for Sponsored Search with Deep Reinforcement Learning
A modified pointer network trained with actor-critic DRL and Equal Size K-Means clustering is applied to combinatorial keyword recommendation in sponsored search, reporting offline and online gains.
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Time-Constrained Recommendations: Reinforcement Learning Strategies for E-Commerce
Reinforcement learning policies for time-constrained slate recommendations improve engagement over contextual bandits in e-commerce settings.