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Thompson Sampling for Personalization: A Hands-On Tutorial

Thompson Sampling is the bandit algorithm that quietly powers recommendation engines at Netflix, LinkedIn, and every serious personalization stack — and it's far simpler to implement than most engineers think.

Hi there,

Most personalization systems A/B test offers until the stats are significant, then lock in the winner. Thompson Sampling does something smarter: it continuously allocates more traffic to better-performing options while still exploring alternatives — in real time, with no test-end date required.

📚 New Tutorial

Thompson Sampling for Personalization: A Hands-On Tutorial

  • What the explore-exploit dilemma is and why A/B testing is the wrong answer for personalization
  • Bayesian foundations: Beta distribution, prior/posterior updates, conjugate pairs
  • Full Python implementation for a multi-arm bandit on credit card offer ranking
  • Contextual bandits: adding user features to Thompson Sampling with Bayesian linear regression
  • Batch vs. real-time update strategies and their production trade-offs
  • Comparison with UCB1 and epsilon-greedy: when to use which
  • Production deployment patterns: Redis-backed parameter store, online updates

Read the full tutorial →

More posts dropping every day. Stay curious.

— Bhanu @ superml.dev