构建可复现的大规模电影推荐多模态数据集，提升研究可信度。
Binge Watch: Reproducible Multimodal Benchmarks Datasets for Large-Scale Movie Recommendation on MovieLens-10M and 20M
- 基于MovieLens-10M/20M扩展多模态特征，含剧情、海报、预告片。
- 使用先进编码器提取特征，公开原始映射与完整数据集。
- 适合做大规模多模态推荐研究的团队与复现实验者使用。
随着多模态推荐系统兴起，高质量含多媒体信息的数据集日益重要。但现有研究多依赖小规模、未记录或非公开数据。本文推出M3L-10M和M3L-20M两个大规模、完全文档化且可复现的数据集，对MovieLens-10M和MovieLens-20M补充多模态特征。通过标准化流程，收集电影剧情、海报与预告片，并用先进编码器提取特征。公开原始数据映射、提取特征及完整数据集，以促进可复现性并推动领域发展。定性与定量分析验证了数据集在多维度上的质量。本工作为大规模多模态电影推荐奠定基础资源。数据集与源码已发布于Zenodo（https://zenodo.org/records/18499145）与GitHub（https://github.com/giuspillo/M3L_10M_20M）。
原文摘要 · Abstract (English)
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.
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