如何自动推荐算法通常工作？ [英] How do automated recommendation algorithms usually work?

问题描述

我刚刚看了怎么团队BellKor的务实混沌是<一个href="http://www.wired.com/epicenter/2009/06/winning-teams-join-to-qualify-for-1-million-netflix-prize/">winning Netflix的挑战中的有线，和我很好奇怎么样算法通常工作。我知道球队Bellkor的解决方案必须是创新的一个在外地..但如何领域通常工作？难道只是一个非常详细的数据库与马尔可夫链被碾过一遍又一遍还是什么？

解决方案

  

但如何领域通常工作？

这是一个数据挖掘技术。数据挖掘作为商业智能（数据仓库等等）的一部分，试图找到在海量数据的关系和信息。这是计算机科学的一个领域，也涉及在一般情况下，如机器学习模式识别。自动建议由关联挖掘了的。具有很高的支持的关联显示为建议。第k近邻算法只是用机器学习/数据挖掘的人很多算法之一。

如果您有兴趣的基础理论，我建议href="http://rads.stackoverflow.com/amzn/click/0120884070">数据挖掘通过伊恩H.威腾。

对于Java有一个伟大的机器学习包， WEKA 这是能够做到的<一个HREF =htt​​p://maya.cs.depaul.edu/~Classes/Ect584/Weka/associate.html>协矿。伊恩·威腾也是WEKA的作者之一。

I just read how team BellKor’s Pragmatic Chaos is winning the Netflix Challenge on Wired, and I'm curious about how this kind of algorithms usually work. I know team Bellkor's solution must be an innovative one on the field.. but how does the field usually work? Is it just a really detailed database with Markov chains being run over again and again or what?

解决方案

but how does the field usually work?

It's a Data Mining technique. Data Mining is used as part of Business Intelligence (Data Warehouse and such) trying to find relations and information in huge amounts of data. It's an area of computer science, dealing also with machine learning in general, e.g. pattern recognition. Automatic recommendations are got by Association Mining. An association with a high support is shown as recommendation. The k-nearest-neighbor algorithm is only one of many algorithms used by machine learning/data mining people.

If you are interested in basic theory, I recommend Data Mining: Practical Machine Learning Tools and Techniques by Ian H. Witten.

For Java there is a great machine learning package, WEKA that is able to do association mining. Ian Witten is also one of the authors of WEKA.

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