了解和构建社交网络算法 [英] Understanding and building a social network algorithm
问题描述
我不确定这是否是问这个问题的合适平台。
但我的问题陈述是：我有一家书店， x没有客户（x很大）。
客户可以告诉我一本书是好是坏（不推荐）。
我有一个内部逻辑将书籍合并在一起，因此，如果客户说一本书不好，他就是说类似的书籍也是不好的，不要向他展示。
我不得不把那些书藏起来。客户之间也可以进行互动，并且彼此之间具有相互信任的程度。
当客户A说Book X1不好时，就会出现这种情况。因此，我将X1，X2，X3，X4等列入黑名单。
但是他的朋友客户B说X3很好。因此，现在我必须将X3展示给A。
我正在考虑建立一个基于所有客户互动的社交网络，并能够计算其相互信任度。
因此，在上述senario中，如果相互信任度很高，则X3会显示为A，否则我不会将X3显示为A。
I想让自己开始建立社交网络并分配wt。到2个节点（我的客户）之间的路径。请给我建议一些我可以开始的好指针。
任何书籍，网站等。
谢谢
从较高的层次上讲，您将需要研究机器学习，数据挖掘和图形挖掘/分析领域。
在机器学习和数据挖掘方面，您将需要研究协作过滤-我推荐这本书。在该领域有很多工作要做，请注意亚马逊之类的网站如何提供一项功能，向您显示购买了哪些其他商品以及您当前正在查看的商品。
在构建社交网络方面，您首先需要确定要使用的数据库系统。存在诸如 Neo4J 和 FlockDB 在设计时考虑了图形。.您也可以选择更通用的东西，例如MySQL，这取决于您要走多远。 / p>
一旦您确定要使用此社会图数据，就可以在其中引入随机游走，社区结构/检测和集中性等概念。我建议您通过本系列 Twitter在加州大学伯克利分校进行的演讲来获得利用社交数据的更好主意。
I am not sure whether this is the right platform to ask this question. But my problem statement is : I have a book shop & x no of clients (x is huge).
A client can tell me whether a book is a good or bad (not recommended). I have a internal logic to club books together , so if a client says a book is bad, he is saying that similar books are bad too and don't show him that.
I oblige and hide those books. Clients can also interact among themselves, and have a mutual confidence level between them. A case arises when client A says Book X1 is bad. Hence i blacklist X1,X2,X3,X4 etc.
But his friend client B says X3 is good. So now i have to show X3 to A. I was thinking to build a social network of all my clients based on their interaction, and be able to calculate their mutual confidence level. So in the above senario if mutual confidence level is very high will will show X3 to A, or else i won't show X3 to A.
I wanted to get myself kickstarted on building the social network and assigning a wt. to a path between 2 nodes (my clients). Please suggest me some good pointers where i can start. Any book, websites etc.
Thanks
From a high level, you will want to look into the fields of Machine Learning, Data Mining, and graph mining/analysis.
In terms of machine learning and data mining, you will want to look into collaborative filtering - I recommend this book. There is a lot of work in this field, notice how websites like Amazon have a feature that shows you what other items were purchased along with the item you are currently looking at.
In terms of building a social network, you will first need to figure out what database system you want to use. There exists graph databases like Neo4J and FlockDB that are designed with graphs in mind.. you may alternatively opt for something more general like MySQL instead, depends on how far you want to go.
Once you have that decided you'll want to leverage this "social graph" data, which is where concepts like random walks, community structure/detection, and centrality come in. I recommend going through this series of lectures Twitter gave at UC Berkeley to get a better idea of leveraging social data.
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