该数据集于 2018 年收集，用于学术研究。它被用于分析和评估内容驱动推荐系统领域中的用户分析方法，在硕士课程中。米兰理工大学计算机科学与工程论文。

Content

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.
The data was acquired by inviting volunteer users and paid ones (through crowdsourcing) to visit our WebApp mints and to selected their favoured movies and related attributes freely, without surveys.

Acknowledgements

The information about movie contents was fetched from TMDb through their APIs.
This product uses the TMDb API but is not endorsed or certified by TMDb.

Inspiration

We hope to fill a bit the gap of datasets containing ratings on features, which are useful to evaluate and improve user profiling methods used in recommender systems.