Educational wine recommendations from initially sparse data

Wineout的目标是通过使用技术来普及对葡萄酒的知识和欣赏。他们在这一领域最初的努力是为非专业葡萄酒饮用者设计一款基于游戏的应用程序，但这需要对葡萄酒特征的初步理解，这超出了大多数消费者的知识范围。为了进一步推进Wineout的使命，公司和大学制定了本项目，以设计和原型开发葡萄酒推荐系统，从有限的用户数据开始，然后根据个人消费者的口味和相似用户的个人资料进行定制。其目标不仅是提供用户可能喜欢的推荐，还有助于教育用户了解葡萄酒的具体特征。这个项目的很大一部分可以被视为传统的机器学习挑战，但可以说还有一个更重要的组成部分与用户体验有关。因此，重要的是要了解该应用程序的目标受众目前是如何选择葡萄酒的，并确保该应用程序支持现有的习惯。

Wineout aims to democratize knowledge and appreciation of wine through the use of technology. Their initialeffort in this area was a game-based app intended for non-expert wine drinkers, but this requires an initialunderstanding of wine characteristics that is beyond the knowledge of most consumers. To further advanceWineout's mission, the present project was formulated between the company and university to design andprototype development of a recommendation system for wines that begins with limited user data, and overtime, becomes tailored to the individual consumer's tastes and profiles of similar users. The objective is notonly to offer recommendations that the user is likely to enjoy, but also to help educate users as to specificcharacteristics of the wines. Much of this project can be viewed as a conventional machine learning challenge,but there is an arguably even more important component that relates to the user experience. Thus, significanteffort will be allocated to gaining an understanding of how the target audience for the app currently makes theirwine selections, and ensuring that the app supports existing habits.