Binge Watch: Reproducible Multimodal Benchmark Datasets for Large-Scale Movie Recommendation on MovieLens-10M and 20M

Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).

As Multimodal Recommender Systems gain interest, high-quality datasets with multimedia side information have become essential. However, much of the current literature reports experiments that rely on small-scale, undocumented, or non-public datasets. In this paper, we introduce M3L-10M and M3L-20M, two large-scale, fully documented and reproducible datasets that enrich MovieLens-10M and MovieLens-20M with multimodal features. Following a documented pipeline, we collect movie plots, posters, and trailers and extract features using state-of-the-art encoders. We publicly release raw data mappings, extracted features, and complete datasets to foster reproducibility and advance the field. Qualitative and quantitative analyses demonstrate the quality of our datasets across multiple perspectives. This work establishes a foundational resource for large-scale, multimodal movie recommendation. Our resource is available at: https://zenodo.org/records/18499145, with source code at https://github.com/giuspillo/M3L_10M_20M.