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A Content-Driven Micro-Video Recommendation Dataset at Scale

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arxiv 2309.15379 v1 pith:5R6UIZRG submitted 2023-09-27 cs.IR

Yongxin Ni , Yu Cheng , Xiangyan Liu , Junchen Fu , Youhua Li , Xiangnan He , Yongfeng Zhang , Fajie Yuan This is my paper
classification cs.IR
keywords micro-videorecommendationdatasetmicrolensvideorecommenderchallengecontent-driven
verification ladder T0 review T1 audit T2 compute T3 formal
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Micro-videos have recently gained immense popularity, sparking critical research in micro-video recommendation with significant implications for the entertainment, advertising, and e-commerce industries. However, the lack of large-scale public micro-video datasets poses a major challenge for developing effective recommender systems. To address this challenge, we introduce a very large micro-video recommendation dataset, named "MicroLens", consisting of one billion user-item interaction behaviors, 34 million users, and one million micro-videos. This dataset also contains various raw modality information about videos, including titles, cover images, audio, and full-length videos. MicroLens serves as a benchmark for content-driven micro-video recommendation, enabling researchers to utilize various modalities of video information for recommendation, rather than relying solely on item IDs or off-the-shelf video features extracted from a pre-trained network. Our benchmarking of multiple recommender models and video encoders on MicroLens has yielded valuable insights into the performance of micro-video recommendation. We believe that this dataset will not only benefit the recommender system community but also promote the development of the video understanding field. Our datasets and code are available at https://github.com/westlake-repl/MicroLens.

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Cited by 13 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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  4. RecBase: Generative Foundation Model Pretraining for Zero-Shot Recommendation

    cs.IR 2025-09 conditional novelty 6.0 of 10

    A from-scratch model that tokenizes items into hierarchical codes and predicts next-item codes reaches higher average zero-shot AUC on 8 datasets than LLM recommenders up to 7B parameters.

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