Optimizing Video Recommender System with Bandit-based Ensemble from Online User Action
Keywords: Recommender System, Rank-based Ensemble, Multi-Armed Bandit, Dirichlet Distribution, Optimizing Ensemble
Abstract: In the modern era of video content platforms like YouTube and Netflix, personalized recommendations are essential. This study introduces an ensemble recommendation model that considers unique platform characteristics. We segment recommendation scenarios and fine-tune models based on real user actions, resulting in personalized recommendation lists. Our contributions include developing recommendation models for each characteristic, the use of the Multi-Armed Bandit (MAB) algorithm for ensemble each model with user feedback, and improved video recommendations with enhanced user satisfaction in real-world services.
Submission Number: 7
Loading