limcf

LimCF, short for Lightweight Incremental Matrix Factorization for Collaborative Filtering, is a modular software library and research framework designed for building recommender systems that update latent user and item factors as new interaction data becomes available. It is optimized for large, sparse rating matrices and online learning scenarios where memory footprint and latency are concerns.

The library provides multiple incremental learning algorithms, including stochastic gradient descent variants and incremental alternating least squares, along with streaming data interfaces, sparse matrix representations, and on-device deployment options. LimCF emphasizes a pluggable architecture: backends, evaluators, and data pipelines can be swapped or extended without rewriting core components. It supports privacy-preserving configurations and can operate with differential privacy mechanisms, when used in production settings.

LimCF's API is designed to be approachable for researchers while suitable for production use. It offers Python bindings for rapid experimentation and a high-performance C++ core for efficient inference. Typical workflows include fitting a model on historical interactions, updating factors in near real time as new events arrive, and evaluating model quality using standard metrics such as RMSE, MAE, and ranking metrics.

History and development: LimCF was developed by researchers and engineers seeking an online-friendly alternative to batch matrix factorization. It has been released under a permissive open-source license and has an active community contributing algorithms, backends, and adapters to common data platforms.

See also: Matrix factorization, Collaborative filtering, Online learning.