"Karrot, a leading platform for building local communities in Korea, uses a recommendation system to provide users with personalized content on the home screen. The system consists of the recommendation machine learning model and a feature platform that acts as a data store for users' behaviour history and article information. As the company has been evolving the recommendation system over recent years, it became apparent that adding new functionality was becoming challenging, and the system began to suffer from limited scalability and poor data quality"
"The initial architecture of the recommendation system was tightly coupled with the flea market web application, with feature-specific code hard-coded. Even though the architecture used scalable data services, such as Amazon Aurora, Amazon ElastiCache, and Amazon S3, sourcing data from multiple data stores led to data inconsistencies and challenges when introducing new content types, such as local community, jobs, and advertisements."
Karrot replaced a tightly coupled, hard-coded recommendation stack with a distributed, event-driven architecture built on scalable AWS services. The legacy system relied on multiple fragmented data stores and feature-specific code, which caused data inconsistencies, limited scalability, and degraded data quality. The new design centralizes user and article features into a unified feature platform that serves as the canonical data store for behavior history and article metadata. The unified feature store and event-driven ingestion improve ML input quality, simplify adding new content types, and increase overall reliability and scalability of personalized recommendations.
Read at InfoQ
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