A modular architecture that separates recommendation components into distinct, interchangeable layers is key for plug-and-play recommendation systems. This design allows developers to swap or upgrade individual modules (like feature extractors, candidate generators, or rankers) without reworking the entire system. The core idea is to standardize interfaces between components, ensuring they communicate via well-defined data formats and protocols. For example, a candidate generation module might output a list of item IDs in a specific schema, which a ranking module then processes regardless of how those candidates were generated. This decoupling enables flexibility in experimentation and deployment.
One practical implementation is the two-stage retrieval-and-ranking pipeline, commonly used in large-scale systems. In the retrieval stage, lightweight modules (like approximate nearest neighbor search or rule-based filters) quickly narrow down candidates from millions to hundreds. These modules can be swapped—for instance, replacing a collaborative filtering-based retriever with a neural embedding model—as long as they adhere to the same input/output interface. The ranking stage then uses more complex models (e.g., deep neural networks) to score the retrieved items. Frameworks like TensorFlow Recommenders (TFRS) support this by providing prebuilt layers for embedding lookup, cross-feature interaction, and ranking losses, which developers can mix and match.
Another approach is microservices-based architecture, where each recommendation component runs as an independent service. For example, a feature engineering service might expose an API that returns user/item embeddings, which a separate ranking service consumes. This allows teams to update the embedding model without affecting the ranking service, as long as the API contract remains stable. Tools like Kubernetes or Docker simplify orchestration, while gRPC or REST APIs handle communication. Companies like Netflix and Spotify use such designs to A/B test recommendation algorithms in production by routing traffic between different module versions. By enforcing strict contracts between modules and using versioned APIs, developers achieve both flexibility and stability in evolving systems.