当前网络人工智能系统的问题
我即将进入人工智能学生的第三年学习，并正在规划我的第三年项目。我一直在考虑某种推荐系统。这样做的动机是了解人们如何评估产品（是什么让产品变得有吸引力），并因此尝试构建一个能够理解这一点的系统。目前我的想法是建立一个能够区分人们喜欢和不喜欢的不同优先级的系统。例如，一个非常注重环保的人可能不会想购买不环保的产品。
所以问题是 - 现代网络人工智能系统（Google、Amazon、Last.fm 等）中哪些东西最需要修复/开发。
我的项目期限大约为 6 个月，但我很想听听关于这个主题的任何想法。
I'm going into my third year of studies as an AI student and am planning my third year project. I have been considering a recommendation system of some sort. The motivation for this is to gain an understanding of how people evaluate products (what makes the products desirable) and consequently attempt to build a system that would understand this. Currently my thinking is along the lines of a system that would be able to differentiate between different priorities in peoples' likes and dislikes. For instance a person who is environmentally very aware probably wouldn't want to buy products that are not.
So the question is
- What things are most in need of repair/development in the modern web AI systems (Google, Amazon, Last.fm and so on).
My project is limited to about 6 months but I would be interested to hear any thought on the subject.
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您可能想要查看的一些内容包括 Facebook OpenSocial Graph 和 Google Prediction API。
Some of the things that you might want to look at are Facebook OpenSocial Graph and Google Prediction API.