ADS-16计算广告数据集，由120位不熟悉的人投票选出的300个真实广告集合

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. 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 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).