Recommender systems (RS) have achieved significant success by leveraging explicit identification (ID) features. However, the full potential of content features, especially the pure image pixel features, remains relatively unexplored. The limited availability of large, diverse, and content-driven image recommendation datasets has hindered the use of raw images as item representations. In this regard, we present PixelRec, a massive image-centric recommendation dataset that includes approximately 200 million user-image interactions, 30 million users, and 400,000 high-quality cover images. By providing direct access to raw image pixels, PixelRec enables recommendation models to learn item representation directly from them. To demonstrate its utility, we begin by presenting the results of several classical pure ID-based baseline models, termed IDNet, trained on PixelRec. Then, to show the effectiveness of the dataset's image features, we substitute the itemID embeddings (from IDNet) with a powerful vision encoder that represents items using their raw image pixels. This new model is dubbed PixelNet.Our findings indicate that even in standard, non-cold start recommendation settings where IDNet is recognized as highly effective, PixelNet can already perform equally well or even better than IDNet. Moreover, PixelNet has several other notable advantages over IDNet, such as being more effective in cold-start and cross-domain recommendation scenarios. These results underscore the importance of visual features in PixelRec. We believe that PixelRec can serve as a critical resource and testing ground for research on recommendation models that emphasize image pixel content. The dataset, code, and leaderboard will be available at https://github.com/website-pixelrec/PixelRec.

翻译：推荐系统（RS）通过利用显式身份（ID）特征取得了显著成功。然而，内容特征，尤其是纯图像像素特征的潜力仍相对未被充分发掘。由于缺乏大规模、多样化且以内容驱动的图像推荐数据集，原始图像作为物品表征的应用受到制约。针对这一问题，我们提出PixelRec——一个以图像为核心的大规模推荐数据集，包含约2亿条用户-图像交互记录、3000万用户及40万张高质量封面图像。通过直接提供原始图像像素，PixelRec使推荐模型能够直接学习物品表征。为展示其效用，我们首先报告了在PixelRec上训练的若干经典纯ID基线模型（称为IDNet）的实验结果。随后，通过使用强大的视觉编码器（以原始图像像素表征物品）替换IDNet中的物品ID嵌入，我们构建了新模型PixelNet。实验表明，即使在IDNet被认为高度有效的标准非冷启动推荐场景中，PixelNet已能达到与IDNet相当甚至更优的性能。此外，PixelNet在冷启动和跨域推荐场景中展现出优于IDNet的显著优势。这些结果凸显了PixelRec中视觉特征的重要性。我们相信PixelRec将成为基于图像像素内容的推荐模型研究的关键资源与测试基准。数据集、代码及排行榜将发布于https://github.com/website-pixelrec/PixelRec。