A personalized reinforcement learning recommendation algorithm using bi-clustering techniques.

ABSTRACT: Recommender systems have become a core component of various online platforms, helping users get relevant information from the abundant digital data. Traditional RSs often generate static recommendations, which may not adapt well to changing user preferences. To address this problem, we propose a novel reinforcement learning (RL) recommendation algorithm that can give personalized recommendations by adapting to changing user preferences. However, a significant drawback of RL-based recommendation systems is that they are computationally expensive. Moreover, these systems often fail to extract local patterns residing within dataset which may result in generation of low quality recommendations. The proposed work utilizes biclustering technique to create an efficient environment for RL agents,

SUBMITTER: Waqar M 

PROVIDER: S-EPMC11841880 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature