Bandit algorithms

DataFunTalk

Aug 27, 2021 · Artificial Intelligence

Hybrid Bandit and Visual-aware Ranking Models for Advertising Creative Selection and Dynamic Optimization

The article presents a hybrid bandit framework combined with a visual‑aware ranking model to efficiently select and dynamically optimize advertising creatives, addressing cold‑start challenges, element‑level personalization, and production‑parameter search, and validates the approach with extensive offline and online experiments.

Bandit algorithmsCTR predictionMachine Learning

0 likes · 15 min read

Hybrid Bandit and Visual-aware Ranking Models for Advertising Creative Selection and Dynamic Optimization

HomeTech

Jun 10, 2020 · Artificial Intelligence

Exploitation & Exploration Algorithms in Recommender Systems: ε‑Greedy, UCB, and Thompson Sampling Applications

This article introduces recommender systems and the exploitation‑exploration dilemma, explains common E&E algorithms such as ε‑greedy, Upper‑Confidence‑Bound, and Thompson Sampling, and details their practical deployment for interest‑point eviction, selection, and adaptive recall count optimization in an automotive recommendation platform.

Bandit algorithmsThompson SamplingUCB

0 likes · 10 min read

Exploitation & Exploration Algorithms in Recommender Systems: ε‑Greedy, UCB, and Thompson Sampling Applications

DataFunTalk

Apr 19, 2020 · Artificial Intelligence

Bandit Algorithms for Recommendation Systems: Context‑Free, Thompson Sampling, and Contextual Approaches

This article explains how multi‑armed bandit methods such as Upper Confidence Bound, Thompson Sampling, and their contextual extensions can address cold‑start, diversity, and bias problems in large‑scale recommendation systems, describing practical update mechanisms, offline evaluation techniques, and deployment experiences at Ctrip.

AIBandit algorithmsContextual Bandits

0 likes · 15 min read

Bandit Algorithms for Recommendation Systems: Context‑Free, Thompson Sampling, and Contextual Approaches