ADS-16 Computational Advertising Dataset

# Context

In the last decade, new ways of shopping online have increased the possibility of buying products and services more easily and faster than ever. In this new context, personality is a key determinant in the decision making of the consumer when shopping. A person's buying choices are influenced by psychological factors like impulsiveness; indeed some consumers may be more susceptible to making impulse purchases than others. Since affective metadata are more closely related to the user's experience than generic parameters, accurate predictions reveal important aspects of user's attitudes, social life, including attitude of others and social identity. This work proposes a highly innovative research that uses a personality perspective to determine the unique associations among the consumer's buying tendency and advert recommendations. In fact, the lack of a publicly available benchmark for computational advertising do not allow both the exploration of this intriguing research direction and the evaluation of recent algorithms. We present the ADS Dataset, a publicly available benchmark consisting of 300 real advertisements (i.e., Rich Media Ads, Image Ads, Text Ads) rated by 120 unacquainted individuals, enriched with Big-Five users' personality factors and 1,200 personal users' pictures.

# Content

The content of the zip files are folders.
The directory tree of this disk is as follows:

20 Ads folder:
Ads belong to 20 product/service categories. all the ads are here.
120 Users Folders:
Each folder contains data for one of the involved subjects.
300 real advertisements have been scored, Ratings according to the users’ interests (1 star to 5 stars), ~1,200 personal pictures (labelled as positive/negative), Big-Five personality scores (O-C-E-A-N).

Data can be easily analysed in Matlab, or Python

# Acknowledgements
If you use our dataset please cite:

[1] Roffo, G., & Vinciarelli, A. (2016, August). Personality in computational advertising: A benchmark. In 4 th Workshop on Emotions and Personality in Personalized Systems (EMPIRE) 2016 (p. 18).

# Inspiration

We collected and introduced a representative benchmark for computational advertising enriched with affective-like metadata such as personality factors. The benchmark allows to (i) explore the relationship between consumer characteristics, attitude toward online shopping and advert recommendation, (ii) identify the underlying dimensions of consumer shopping motivations and attitudes toward online in-store conversions, and (iii) have a reference benchmark for comparison of state-of-the-art advertisement recommender systems (ARSs). To the best of our knowledge, the ADS dataset is the first attempt at providing a set of advertisements scored by the users according to their interest into the content.
We hope that this work motivates researchers to take into account the use of personality factors as an integral part of their future work, since there is a high potential that incorporating these kind of users' characteristics into ARS could enhance recommendation quality and user experience.

在过去十年中，网络购物的全新模式极大地提升了消费者购买产品与服务的便捷性与速度。在此背景下，个性因素成为消费者购物决策的关键决定因素。个体的购买选择受心理因素如冲动性的影响；实际上，某些消费者可能比其他人更容易受到冲动购买的影响。鉴于情感元数据与用户体验的关系比通用参数更为紧密，准确的预测揭示了用户态度、社交生活的重要方面，包括他人的态度和社会身份。本研究提出了一项极具创新性的研究，采用个性视角来探究消费者购买倾向与广告推荐之间的独特关联。事实上，由于缺乏公开可用的计算广告基准，无法探索这一引人入胜的研究方向，也无法评估近期算法。我们呈现了ADS数据集，这是一个公开可用的基准数据集，包含由120名不熟悉的个体评定的300条真实广告（包括富媒体广告、图像广告、文本广告），并丰富了五大人格特质用户因素和1200张个人用户照片。

数据集内容以压缩文件形式提供，其中包含文件夹。
磁盘目录结构如下：
20个广告文件夹：
包含20个产品/服务类别的广告，所有广告均在此处。
120个用户文件夹：
每个文件夹包含涉及主题之一的数据。
300条真实广告已评分，评分依据用户兴趣（1星到5星），约1200张个人照片（标记为正面/负面），五大人格特质评分（O-C-E-A-N）。
数据可轻松使用Matlab或Python进行分析。

致谢：
如使用我们的数据集，请引用：
[1] Roffo, G., & Vinciarelli, A. (2016, August). Personality in computational advertising: A benchmark. In 4th Workshop on Emotions and Personality in Personalized Systems (EMPIRE) 2016 (p. 18).

灵感：
我们收集并引入了一个代表性基准数据集，该数据集富含情感类元数据，如人格因素。该基准允许（i）探究消费者特征、对在线购物的态度与广告推荐之间的关系，（ii）识别消费者购物动机和在线店内转换态度的潜在维度，（iii）为最先进的广告推荐系统（ARS）的比较提供一个参考基准。据我们所知，ADS数据集是第一个尝试提供一组用户根据对内容兴趣评分的广告的数据集。我们希望这项工作能够激励研究人员在未来的工作中将人格因素作为其研究不可或缺的一部分，因为将这些用户的特征纳入ARS有很高的潜力，可以提升推荐质量和用户体验。

搜集汇总

背景与挑战

背景概述

ADS-16计算广告数据集是一个公开基准，包含300个真实广告（涵盖富媒体、图像和文本广告），由120个互不熟悉的用户根据兴趣进行1-5星评分。数据集还提供了用户的大五人格因素和约1200张标记的个人图片，旨在研究消费者人格特征与广告推荐之间的关联，为计算广告算法评估提供参考。