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

TL;DR

AntM$^{2}$C is the largest multi-scenario, multi-modal CTR dataset derived from industrial data, enabling comprehensive evaluation of recommendation models across diverse item types and features.

Contribution

The paper introduces AntM$^{2}$C, a large-scale, multi-scenario, multi-modal CTR dataset with 1 billion data points, covering multiple item types and including multi-modal features, filling significant gaps in existing datasets.

Findings

AntM$^{2}$C includes 5 item types, offering diverse user preference insights.

The dataset incorporates multi-modal features like text and images, enhancing modeling capabilities.

It is the largest-scale CTR dataset with 1 billion entries, enabling more reliable evaluation.

Abstract

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 https://www.atecup.cn/home.