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

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 AlgorithmsExploration‑exploitation

0 likes · 15 min read

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

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