Click-through rate (CTR) prediction is a crucial issue in recommendation
systems. There has been an emergence of various public CTR datasets. However,
existing datasets primarily suffer from the following limitations. Firstly,
users generally click different types of items from multiple scenarios, and
modeling from multiple scenarios can provide a more comprehensive understanding
of users. Existing datasets only include data for the same type of items from a
single scenario. Secondly, multi-modal features are essential in multi-scenario
prediction as they address the issue of inconsistent ID encoding between
different scenarios. The existing datasets are based on ID features and lack
multi-modal features. Third, a large-scale dataset can provide a more reliable
evaluation of models, fully reflecting the performance differences between
models. The scale of existing datasets is around 100 million, which is
relatively small compared to the real-world CTR prediction. To address these
limitations, we propose AntM$^{2}$C, a Multi-Scenario Multi-Modal CTR dataset
based on industrial data from Alipay. Specifically, AntM$^{2}$C provides the
following advantages: 1) It covers CTR data of 5 different types of items,
providing insights into the preferences of users for different items, including
advertisements, vouchers, mini-programs, contents, and videos. 2) Apart from
ID-based features, AntM$^{2}$C also provides 2 multi-modal features, raw text
and image features, which can effectively establish connections between items
with different IDs. 3) AntM$^{2}$C provides 1 billion CTR data with 200
features, including 200 million users and 6 million items. It is currently the
largest-scale CTR dataset available. Based on AntM$^{2}$C, we construct several
typical CTR tasks and provide comparisons with baseline methods. The dataset
homepage is available at this https URL

基于 AntM$^{2}$C，本文提出了多场景多模态点击率数据集，涵盖广告、优惠券、小程序、内容和视频等 5 种不同类型的项目，提供了除基于 ID 的特征外，原始文本和图像特征的多模态特征，共有 10 亿次点击率数据，是目前最大规模的点击率数据集之一。