Eugene Yan • 5/8/2022

Bandits for Recommender Systems

Read Original

This technical article explains how bandit algorithms address the cold-start and feedback loop problems in recommender systems. It details three core algorithms—ε-greedy, Upper Confidence Bound (UCB), and Thompson Sampling—and discusses their industrial applications for dynamic item sets like news and ads, focusing on reducing regret through adaptive exploration.

0 comments
#Machine Learning #Reinforcement Learning #Recommender Systems
Bandits for Recommender Systems

Comments

No comments yet

Be the first to share your thoughts!

Browser Extension

Get instant access to AllDevBlogs from your browser

Top of the Week

1
React, useEffect, and Stale Closures: How to Avoid Them with Dependencies and useRef
Jivbcoop • 4 votes
2
Better react-hook-form Smart Form Components
Maarten Hus • 2 votes
3
AGI, ASI, A*I – Do we have all we need to get there?
John D. Cook • 1 votes
4
Quoting Thariq Shihipar
Simon Willison • 1 votes
5
Dew Drop – January 15, 2026 (#4583)
Alvin Ashcraft • 1 votes
6
Using Browser Apis In React Practical Guide
Jivbcoop • 1 votes