Recommender System on Jaehyeon Kimhttps://jaehyeon.me/tags/recommender-system/Recent content in Recommender System on Jaehyeon KimHugo -- gohugo.ioenCopyright © 2023-2026 Jaehyeon Kim. All Rights Reserved.Mon, 23 Feb 2026 00:00:00 +0000Productionizing an Online Product Recommender using Event Driven Architecturehttps://jaehyeon.me/blog/2026-02-23-productionize-recommender-with-eda/Mon, 23 Feb 2026 00:00:00 +0000https://jaehyeon.me/blog/2026-02-23-productionize-recommender-with-eda/In Part 1, we built a contextual bandit prototype using Python and Mab2Rec. While effective for testing algorithms locally, a monolithic script cannot handle production scale. Real-world recommendation systems require low-latency inference for users and high-throughput training for model updates.

This post demonstrates how to decouple these concerns using an event-driven architecture with Apache Flink, Kafka, and Redis.

]]>Prototyping an Online Product Recommender in Pythonhttps://jaehyeon.me/blog/2026-01-29-prototype-recommender-with-python/Tue, 27 Jan 2026 00:00:00 +0000https://jaehyeon.me/blog/2026-01-29-prototype-recommender-with-python/Overview Traditional recommendation approaches such as Collaborative Filtering remain widely adopted, yet they come with notable constraints. They are particularly vulnerable to the cold-start problem, where new users lack sufficient interaction history, and they depend heavily on long-term behavioral data. As a result, they frequently overlook real-time contextual signals, including time of day, device type, location, or session intent. This can prevent them from capturing situational preferences, such as someone preferring coffee in the morning but pizza in the evening.