Intelligent Creative Generation and Optimization for Xiaohongshu Advertising

Xiaohongshu’s end‑to‑end intelligent creative platform extracts high‑quality images, generates diverse titles with RED‑pretrained GPT‑2/T5 models, and selects the best ads using a UCB‑based multi‑armed bandit that balances CTR uplift, revenue and user‑experience, while employing position‑corrected metrics and a scalable dual‑tower DNN to boost long‑tail performance and overall revenue.

AIAdvertisingNLP

0 likes · 18 min read

Intelligent Creative Generation and Optimization for Xiaohongshu Advertising

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 AlgorithmsEpsilon-GreedyExploitation

0 likes · 10 min read

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

21CTO

Jul 1, 2017 · Product Management

Why Simple Click Counts Fail: Smarter Scoring Strategies for Content Recommendation

The article recounts a junior engineer's journey improving a news app's recommendation system, moving from naive click counts to recent clicks, CTR, lower confidence bounds, and advanced multi‑armed bandit techniques like UCB and Thompson Sampling to balance relevance and novelty.

CTRLCBThompson Sampling

0 likes · 9 min read

Why Simple Click Counts Fail: Smarter Scoring Strategies for Content Recommendation