Abstract: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: this https URL, with source code at this https URL.
From: Giuseppe Spillo [view email]
[v1]
Tue, 17 Feb 2026 11:22:20 UTC (4,749 KB)
[v2]
Mon, 27 Jul 2026 15:41:06 UTC (4,997 KB)
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