An Image Dataset for Benchmarking Recommender Systems with Raw Pixels
Yu Cheng, Yunzhu Pan, Jiaqi Zhang, Yongxin Ni, Aixin Sun, Fajie Yuan
Abstract
The advent of large language models has inspired active and promising research focused on developing text content-based recommendation models. Meanwhile, although image features are also key signals in recommender systems, there is currently a lack of research on recommendation models that are primarily based on raw image pixels. The lack of large-scale datasets containing raw images in visually driven recommendation scenarios has been a significant barrier to the development of this research direction. To address this challenge, we introduce PixelRec, a comprehensive dataset of cover images collected from a video streaming platform. With approximately 200 million user image interactions, 30 million users, and 400,000 high-resolution short video cover images, PixelRec facilitates the development, benchmarking, and analysis of various image pixel based recommendation models. Leveraging this dataset, we establish a accessible pipeline to implement a series of vision-based recommendation models, providing extensive benchmark results for them. Our contributions include the PixelRec dataset, baseline algorithms, operational pipeline, exploratory findings, and the PixelRec benchmark. We believe PixelRec will significantly advance research on recommendation models based on image content and foster fruitful collaboration between the fields of recommender systems and computer vision. The dataset, code, and documents are made available at https://github.com/westlake-repl/PixelRec.