Recommender

The Recommender Operator is a low-code template built around the Surprise SVD algorithm for collaborative filtering use cases. It currently focuses on matrix factorization scenarios, and additional capabilities will be documented as they become available.

Current Capabilities

  • Data inputs: expects three tabular sources named users, items, and interactions. Each source can be loaded from local files, OCI Object Storage (oci:// URIs), or database queries using the standard ADS InputData configuration.

  • Model: wraps Surprise SVD with sensible defaults. spec.model_name is reserved for future extensibility and is pinned to svd internally.

  • Outputs: generates a recommendations CSV (recommendations.csv by default) and, when enabled, an HTML summary report.

  • Configuration essentials: top_k, user_column, item_column, and interaction_column are mandatory and map your datasets to the operator.

  • Deployment targets: supports local execution and OCI Data Science Jobs; see Quick Start for the CLI flow and Scalability for production guidance.

Future Updates

New capabilities—such as alternative algorithms, advanced tuning controls, or expanded deployment guidance—will be documented in this guide as they are released.

Added in version 2.11.14.