blocks|key|1138363|text|在优秀的编程集体智能中有许多简单的和不那么简单的建议算法示例。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|1138364|皮尔逊相关系数+(维基百科一篇枯燥无味的文章)可以给出相当好的结果。下面是Python中的一个实现，TSQL中的另一个实现，以及对算法的有趣解释。|1138365|entityMap|0|LINK|mutability|MUTABLE|url|https://rads.stackoverflow.com/amzn/click/com/0596529325|1|http://en.wikipedia.org/wiki/Pearson_product-moment_correlation_coefficient|2|http://www.dreamincode.net/code/snippet3042.htm|3|http://devlicio.us/blogs/billy_mccafferty/archive/2006/11/07/netflix-memoirs-using-the-pearson-correlation-coefficient.aspx^0|4|6|0|0|0|7|1|11|6|2|1E|4|3|0^^$0|@$1|2|3|4|5|6|7|T|8|@]|9|@$A|U|B|V|1|W]]|C|$]]|$1|D|3|E|5|6|7|X|8|@]|9|@$A|Y|B|Z|1|10]|$A|11|B|12|1|13]|$A|14|B|15|1|16]]|C|$]]|$1|F|3|-4|5|6|7|17|8|@]|9|@]|C|$]]]|G|$H|$5|I|J|K|C|$L|M]]|N|$5|I|J|K|C|$L|O]]|P|$5|I|J|K|C|$L|Q]]|R|$5|I|J|K|C|$L|S]]]]

There are many simple and not so simple examples of suggestion algorithms in the excellent 
<a href="https://rads.stackoverflow.com/amzn/click/com/0596529325" rel="noreferrer" rel="nofollow noreferrer">Programming Collective Intelligence</a>

The <a href="http://en.wikipedia.org/wiki/Pearson_product-moment_correlation_coefficient" rel="noreferrer">Pearson correlation coefficient</a> (a little dry Wikipedia article) can give pretty good results. Here's an implementation in <a href="http://www.dreamincode.net/code/snippet3042.htm" rel="noreferrer">Python</a> and another in <a href="http://devlicio.us/blogs/billy_mccafferty/archive/2006/11/07/netflix-memoirs-using-the-pearson-correlation-coefficient.aspx" rel="noreferrer">TSQL</a> along with an interesting explanation of the algorithm.

blocks|key|2739741|text|尝试一种坡度一号算法，它是解决这类问题的最常用算法之一。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|2739742|下面是是t中的一个示例实现|2739743|entityMap|0|LINK|mutability|MUTABLE|url|http://en.wikipedia.org/wiki/Slope_One|1|https://web.archive.org/web/20150107011957/http://blog.charliezhu.com/2008/07/21/implementing-slope-one-in-t-sql^0|4|4|0|0|0|3|1|0^^$0|@$1|2|3|4|5|6|7|P|8|@]|9|@$A|Q|B|R|1|S]]|C|$]]|$1|D|3|E|5|6|7|T|8|@]|9|@$A|U|B|V|1|W]]|C|$]]|$1|F|3|-4|5|6|7|X|8|@]|9|@]|C|$]]]|G|$H|$5|I|J|K|C|$L|M]]|N|$5|I|J|K|C|$L|O]]]]

try a <a href="http://en.wikipedia.org/wiki/Slope_One" rel="nofollow noreferrer">Slope One</a> algorithm, it's one of the most used for this kind of problem.

<a href="https://web.archive.org/web/20150107011957/http://blog.charliezhu.com/2008/07/21/implementing-slope-one-in-t-sql" rel="nofollow noreferrer">here's</a> a sample implementation in t-sql

blocks|key|1216321|text|你可能想看看关联规则学习和先验算法。背后的基本想法是，您创建类似于“如果用户喜欢Object1，而不是用户喜欢Object2”的规则，并检查它们如何很好地描述(您的)现实。在您的具体示例中，此规则支持2(作为Object1这样的两个用户)和50%25的置信度a(因为规则在2种情况中有1种是正确的)。我自己刚刚实现了概念的基本证明(实际上是我在Hadoop上的第一步)，这并不困难。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|1216322|或者，您可能想看看阿帕奇马赫特-品味。不过我自己从来没用过。|1216323|entityMap|0|LINK|mutability|MUTABLE|url|http://en.wikipedia.org/wiki/Association_rule_learning|1|http://en.wikipedia.org/wiki/Apriori_algorithm|2|https://mahout.apache.org/users/recommender/recommender-documentation.html^0|6|6|0|D|4|1|0|9|9|2|0^^$0|@$1|2|3|4|5|6|7|R|8|@]|9|@$A|S|B|T|1|U]|$A|V|B|W|1|X]]|C|$]]|$1|D|3|E|5|6|7|Y|8|@]|9|@$A|Z|B|10|1|11]]|C|$]]|$1|F|3|-4|5|6|7|12|8|@]|9|@]|C|$]]]|G|$H|$5|I|J|K|C|$L|M]]|N|$5|I|J|K|C|$L|O]]|P|$5|I|J|K|C|$L|Q]]]]

You may wanna look at <a href="http://en.wikipedia.org/wiki/Association_rule_learning" rel="nofollow noreferrer">Association rule learning</a> and <a href="http://en.wikipedia.org/wiki/Apriori_algorithm" rel="nofollow noreferrer">Apriori algorithm</a>. The basic idea behind is is that you create rules like "if User like Object1, than User likes Object2" and check how well they describe (your) reality. In your concrete example, this rule would have a Support of 2 (as two Users like Object1) and a confidence of 50% a (as the rule is true in 1 of 2 cases). I've just implemented a basic proofe of concept myself (actually my first steps on Hadoop) and it's not too difficult to do.

Alternatively, you may wanna look at <a href="https://mahout.apache.org/users/recommender/recommender-documentation.html" rel="nofollow noreferrer">Apache Mahout - Taste</a>. I did't ever use it myself though.

blocks|key|2739782|text|我会和K个最近的邻居一起去。维基百科条目很好地解释了它，并有指向引用实现的链接。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|2739783|entityMap|0|LINK|mutability|MUTABLE|url|http://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm^0|E|6|0|0^^$0|@$1|2|3|4|5|6|7|L|8|@]|9|@$A|M|B|N|1|O]]|C|$]]|$1|D|3|-4|5|6|7|P|8|@]|9|@]|C|$]]]|E|$F|$5|G|H|I|C|$J|K]]]]

I would go with K nearest neighbors. The <a href="http://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm" rel="nofollow noreferrer">wikipedia entry</a> explains it well, and has links to reference implementations.

blocks|key|2739791|text|K-最近邻算法|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2739792|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|D|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|E|8|@]|9|@]|A|$]]]|C|$]]

blocks|key|1216429|text|我创建了一个建议的文章算法，它使用关键字(而不是“产品购买”)来确定相关性。它接受一个关键字，并在出现该关键字的所有其他文章中运行，并生成基于哪一篇文章具有最匹配的关键字的结果。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1216430|除了明显需要缓存这些信息之外，他使用类似的方法有什么问题吗？|1216431|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|F|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|G|8|@]|9|@]|A|$]]|$1|D|3|-4|5|6|7|H|8|@]|9|@]|A|$]]]|E|$]]

I created a suggested articles algorithm that used keywords (as opposed to "product purchases") to determine correlation. It takes a keyword, and runs through all other articles where that keyword occurs and produces results based on which articles have the most matching keywords.

Besides the obvious need for caching such information, is there something wrong with him using a similar method?

I am looking for a simple suggestion algorithm to implement in to my Web App. Much like Netflix, Amazon, etc... But simpler. I don't need teams of Phd's working to get a better suggestion metric. 

So say I have:

<ul>
<li>User1 likes Object1.</li>
<li>User2 likes Object1 and Object2.</li>
</ul>

I want to suggest to User1 they might also like Object2.

I can obviously come up with something naive. I'm looking for something vetted and easily implemented.

Simple Suggestion / Recommendation Algorithm

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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我正在寻找一个简单的建议算法来实现到我的Web应用程序。就像Netflix亚马逊等等..。但更简单。我不需要博士团队来获得更好的建议指标。所以说我有：User1喜欢Object1。User2喜欢Object1和Object2。我想向User1建议他们可能也喜欢Object2。我显然可以想出一些天真的东西。我正在寻找一些经过审查和易于实施的东西。

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