OmniRec: The All-In-One Solution for Reproducible and Interoperable Recommender Systems Experimentation

Recommender systems researchers rely heavily on general-purpose libraries that facilitate data preprocessing, model training, and evaluation. However, existing frameworks often suffer from fragmented data handling, inconsistent preprocessing, limited interoperability, and poor dataset referencing, which hinder reproducibility and comparability between studies. We present OmniRec, an open-source Python library designed to address these limitations. OmniRec provides standardized access to more than 230 datasets, a unified and flexible preprocessing pipeline, and seamless integration with multiple state-of-the-art recommender system frameworks, including RecPack, RecBole, Lenskit, and Elliot. Its modular architecture allows researchers to easily integrate new datasets, customize preprocessing steps, and external model interfaces. By combining ease of use, transparency, and reproducibility, OmniRec simplifies experimentation and fosters a more open and collaborative ecosystem for recommender systems research and practice.