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Click-through rate (CTR) prediction is a crucial issue in recommendationsystems. 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, andmodeling from multiple scenarios can provide a more comprehensive understandingof users. Existing datasets only include data for the same type of items from asingle scenario. Secondly, multi-modal features are essential in multi-scenarioprediction as they address the issue of inconsistent ID encoding betweendifferent scenarios. The existing datasets are based on ID features and lackmulti-modal features. Third, a large-scale dataset can provide a more reliableevaluation of models, fully reflecting the performance differences betweenmodels. The scale of existing datasets is around 100 million, which isrelatively small compared to the real-world CTR prediction. To address theselimitations, we propose AntM$^{2}$C, a Multi-Scenario Multi-Modal CTR datasetbased on industrial data from Alipay. Specifically, AntM$^{2}$C provides thefollowing advantages: 1) It covers CTR data of 5 different types of items,providing insights into the preferences of users for different items, includingadvertisements, vouchers, mini-programs, contents, and videos. 2) Apart fromID-based features, AntM$^{2}$C also provides 2 multi-modal features, raw textand image features, which can effectively establish connections between itemswith different IDs. 3) AntM$^{2}$C provides 1 billion CTR data with 200features, including 200 million users and 6 million items. It is currently thelargest-scale CTR dataset available. Based on AntM$^{2}$C, we construct severaltypical CTR tasks and provide comparisons with baseline methods. The datasethomepage is available at https://www.atecup.cn/home.

AntM$^{2}$C: A Large Scale Dataset For Multi-Scenario Multi-Modal CTR Prediction