Prototyping a Real-Time Product Recommender using Contextual Bandits

Hi everyone,

I am writing a blog series on implementing real-time recommender systems. Part 1 covers the theoretical implementation and prototyping of a Contextual Bandit system.

Contextual Bandits optimize recommendations by considering the current “state” (context) of the user and the item. Unlike standard A/B testing or global popularity models, bandits update their internal confidence bounds after every interaction. This allows the system to learn distinct preferences for different contexts (e.g., Morning vs. Evening) without waiting for a daily retraining job.

In Part 1, I discuss:

• Feature Engineering: Constructing context vectors that combine static user attributes with dynamic event features (e.g., timestamps), alongside item embeddings.
• Offline Policy Evaluation: Benchmarking algorithms like LinUCB against Random and Popularity baselines using historical logs to validate ranking logic.
• Simulation Loop: Implementing a local feedback loop to demonstrate how the model “reverse-engineers” hidden logic, such as time-based purchasing habits.

Looking Ahead:

This prototype lays the groundwork for Part 2, where I will discuss scaling this logic using an Event-Driven Architecture with Flink, Kafka, and Redis.

Link to Post: https://jaehyeon.me/blog/2026-01-29-prototype-recommender-with-python/

I welcome any feedback on the product recommender.