如何通过基础统计数据预测用户行为？
假设如下：
类别“电子产品”包含产品“Bluerays”以及其他产品。
当用户浏览“电子产品”时，我可以实施哪些基本统计来推荐更多“蓝光”？现在我只有一个蹩脚的信息“该用户在电子产品类别下购买了五分之三的蓝光”，因此可能性为 60% - 推荐更多蓝光。
编辑： 如果我从卖家的角度想自动填写输入框怎么办？示例：如果卖家通常销售二手蓝光，我想在下次他在“电子产品”下销售时自动填写“条件”字段以增强用户体验？
Assume the following:
Category "Electronics" contains product "Bluerays" among other products.
What are some basic statistics I can implement to recommend more "Bluerays" when the user browses under Electronics? Right now I just have a lame "Bluerays were bought 3 out of 5 times under the Electronics category for this user" so the likelihood is 60% - recommend more Bluerays.
EDIT:
What if I'm coming from a seller's perspective where I want to auto-fill the input box? Example: If the seller usually sells Bluerays used, I want to auto-fill the "condition" field the next time he sells under "Electronics" to enhance the user experience?
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这是一个相当有深度的话题。我将开始对基于协作项目的过滤或基于协作用户的过滤进行一些搜索。
这些是关于该主题的一些好书：
集体智能编程
http://my.safaribooksonline.com/book/web-development/9780596529321
集体智慧在行动
http://my.safaribooksonline.com/book/programming/java/9781933988313
This is a pretty deep topic. I'd start doing some searches for collaborative item based filtering, or collaborative user based filtering.
These are some good books on the subject:
Programming Collective Intelligence
http://my.safaribooksonline.com/book/web-development/9780596529321
Collective Intelligence In Action
http://my.safaribooksonline.com/book/programming/java/9781933988313
您的问题是关于推荐系统的。您有兴趣寻找相似之处来帮助您提出好的建议。这些相似性可以通过几种不同的方式来衡量。最常见的是考虑在您的网站上购买过商品的人过去的行为并寻找他们之间的相似之处。这可以通过使用产品向量之间的简单相关性来完成。如果您还有有关通常在您的网站上购物的人员（年龄、性别）的数据，您可以使用此类信息来改进您的推荐系统。此外，评分系统（喜欢和不喜欢）提供了一条有价值的信息。除了相关性（如果你想考虑其他简单的度量，但不一定是统计度量），你还可以使用欧几里得距离、明可夫斯基距离、向量角度的余弦等等......
如果你的向量的维度很高，您可以考虑减少它的维数，仅包括重要的组件。这可以使用PCA（主成分分析）或奇异值分解。
但是，如果您考虑真正改进系统，则应该考虑使用分类器，例如 最近邻居、决策树 或 支持向量机并使用它们来发现买家的类别。例如，这可以帮助您了解给定的买家是否喜欢便宜的或广泛的品牌...
最后，您可以使用 多臂强盗。
有一些书籍可以帮助您：
1) 推荐系统
2) < a href="https://rads.stackoverflow.com/amzn/click/com/1449341330" rel="nofollow noreferrer">Bandit 算法
3) 机器学习
Your question is about recommender systems. You are interesting in finding similarities that can help you to make good recommendations. These similarities can be measured in several different ways. The most common is to consider the past behavior of the people that have bought in your site and to seek similarities among them. This can be done using simple correlation among the vectors of products. If you have also data about the people (age,gender) that usually buy in your site, you can use this kind of information to improve your recommender system. Furthemore, a valuable piece of information is the one provided by rate systems (like and deslikes). Besides correlation (if you want to consider other simple measures, but not necessarily statistical ones) you can also use Euclidean distance, Minkowski distance, the cosine of the angle of the vectors and so on...
If the dimension of your vector is high, you may consider to reduce the dimmension of it including only the important components. This can be done using PCA (Principal Component Analysis) or Singular Value Decomposition.
However, if you consider to really improve your system you should consider using classifiers such as Nearest Neighbors, Decision Trees or support vector machines and using them to discover the class of your buyers. For instance, this can help you to know if a given buyer preffers cheap or expansive brands...
Finally, you can make online experiments using multi-armed bandit.
There are some books that can help you:
1) Recommender Systems
2) Bandit algorithm
3) Machine Learning
也许涉及其他类似的用户。例如，75% 购买蓝光的用户也会购买 DVD Rack，因此当有人购买蓝光时，向他们建议 DVD Rack 的想法。
Perhaps involve other similar users. For instance, 75% of users who buy bluerays, also buy DVD Racks, so when someone buys a blueray, suggest to them the idea of a DVD Rack.