【科研数据分享】CTR预估数据集汇总

数据作为支撑AI技术发展的基础要素，其重要性不言而喻，高质量的科研数据集对领域技术的发展起着重要的推动作用。天池数据集致力于提供优质的科研数据，以帮助算法从业人员更好地开展模型研究。

本期天池君为大家推荐了5个常用的点击率预估 (CTR estimation) 数据集，点击率预估 (CTR estimation) 是在线信息系统的核心模块之一，是推荐系统、付费广告、搜索引擎重要的组成部分，广泛的应用于商品购物、短视频、本地生活等领域中，与人们的生活息息相关，具有重要的业务价值。随着深度学习的广泛应用，深度点击率预估模型被广泛用于工业界的线上系统中。

本文整理了学术界/业界公用的CTR预估数据集，方便算法研发人员学习。

1►

Kaggle Display Advertising Challenge Dataset by Criteo

简介：This dataset is provided by Criteo, and it contains feature values and click feedback for millions of display ads. Its purpose is to benchmark algorithms for clickthrough rate (CTR) prediction.

官网下载地址：

https://ailab.criteo.com/ressources/

天池下载地址：

https://tianchi.aliyun.com/dataset/144733

2►

Criteo 1TB Click Logs Dataset

简介：This dataset contains feature values and click feedback for millions of display ads. Its purpose is to benchmark algorithms for clickthrough rate (CTR) prediction. It is similar, but larger, to the dataset released for the Display Advertising Challenge hosted by Kaggle.

官网下载地址：

https://ailab.criteo.com/download-criteo-1tb-click-logs-dataset/

天池下载地址：

https://tianchi.aliyun.com/dataset/144736

3►

Amazon Product Data

简介：This dataset contains product reviews and metadata from Amazon, including 233.1 million reviews spanning May 1996 - Oct 2018. This dataset includes reviews (ratings, text, helpfulness votes), product metadata (descriptions, category information, price, brand, and image features), and links (also viewed/also bought graphs).

参考论文：

Justifying recommendations using distantly-labeled reviews and fined-grained aspects.

Jianmo Ni, Jiacheng Li, Julian McAuley. Empirical Methods in Natural Language Processing (EMNLP), 2019

官方下载地址：

https://nijianmo.github.io/amazon/index.html

天池下载地址（图书类目）：

https://tianchi.aliyun.com/dataset/145340

4►

淘宝展示广告点击率预估数据集

简介：Ali_Display_Ad_Click是阿里巴巴提供的一个淘宝展示广告点击率预估数据集。

参考论文：

1. Gai K, Zhu X, Li H, et al. Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction[J]. arXiv preprint arXiv:1704.05194, 2017. 2. Guorui Zhou, Chengru Song, Xiaoqiang Zhu, et al. Deep Interest Network for Click-Through Rate Prediction. arXiv preprint arXiv:1706.06978, 2017.

下载地址：

https://tianchi.aliyun.com/dataset/56

5►

饿了么推荐数据集

简介：The dataset is constructed by click logs from ele.me online recommendation system, including 8 days' data with 146 million sample records.

下载地址：

https://tianchi.aliyun.com/dataset/131047