RecAlike

RecAlike is a software library and platform for building and deploying personalized recommendation systems and item similarity analyses. It provides a range of algorithms, including collaborative filtering, content-based filtering, and hybrid approaches, with implementations for user-based and item-based methods, matrix factorization, neighborhood methods, and feature-based scoring. The design emphasizes modularity and extensibility: a core engine handles data flow and model orchestration, while pluggable components cover data ingestion, feature extraction, model training, evaluation, and serving. It supports batch and real-time scoring and scales to large catalogs through configurable backends and caching.

RecAlike exposes multi-language APIs, including Python and JavaScript bindings, along with a REST interface for deployment in services. The typical data model includes users, items, interactions, and item metadata, and it supports both explicit feedback (ratings) and implicit feedback (views, clicks). Cold-start handling leverages content features and hybrid methods. The platform ships with documentation, example workflows, and evaluation metrics such as precision, recall, NDCG, and AUC.

In practice, RecAlike is used by developers in e-commerce, media, and knowledge-graph applications to improve discovery and engagement. The project is maintained as an open-source project under a permissive license, with an emphasis on clear APIs, test coverage, and community contributions. Limitations and considerations commonly discussed include bias and fairness, data governance, and the need for monitoring in production deployments.