使用先验算法建议 [英] Using the apriori algorithm for recommendations

问题描述

因此​​，一个最近的问题让我知道了，而凉爽的 apriori算法。我明白为什么它的工作原理，但什么我不知道的是实际应用。 presumably的主要原因计算相关项目集是能够提供基于自己的购买（或自备物品，等等），有人建议。但是你如何从一组相关的项集个人建议去了？

So a recent question made me aware of the rather cool apriori algorithm. I can see why it works, but what I'm not sure about is practical uses. Presumably the main reason to compute related sets of items is to be able to provide recommendations for someone based on their own purchases (or owned items, etcetera). But how do you go from a set of related sets of items to individual recommendations?

维基百科的文章完成：

的第二个问题是生成   从这些大的关联规则   项集与的约束   最小的信心。假设中的一个   大项目集是路，路= {I1，I2，...   ，益}，联想本细则   在生成项目集   方式如下：第一条规则是{I1，   I2，...，益1} {⇒谥}，通过检查   相信这条规则可以被确定   有趣与否。然后其他规则   删去最后产生   在前提和插入项目   它随之而来的，进一步   新规则置信度   检查，以确定   兴趣度他们。那些   过程重复直到   前因变空

The second problem is to generate association rules from those large itemsets with the constraints of minimal confidence. Suppose one of the large itemsets is Lk, Lk = {I1, I2, … , Ik}, association rules with this itemsets are generated in the following way: the first rule is {I1, I2, … , Ik-1}⇒ {Ik}, by checking the confidence this rule can be determined as interesting or not. Then other rule are generated by deleting the last items in the antecedent and inserting it to the consequent, further the confidences of the new rules are checked to determine the interestingness of them. Those processes iterated until the antecedent becomes empty

我不知道如何设置的关联规则有助于确定建议的最佳设置要么，但。也许我错过了点，先验不适合这种用途？在这种情况下，什么样的是的打算呢？

I'm not sure how the set of association rules helps in determining the best set of recommendations either, though. Perhaps I'm missing the point, and apriori is not intended for this use? In which case, what is it intended for?

推荐答案

所以先验算法不再是艺术的状态的购物篮分析的（又名 的关联规则挖掘的）。该技术得到了改善，但先验原则（即一个子集上界集的支持）的支持仍是一个推动力。

So the apriori algorithm is no longer the state of the art for Market Basket Analysis (aka Association Rule Mining). The techniques have improved, though the Apriori principle (that the support of a subset upper bounds the support of the set) is still a driving force.

在任何情况下，关联规则被用于产生建议的方式是这样的，给定的一些历史项集，我们可以检查每个规则的antecedant以查看是否包含在历史。如果是这样，那么我们可以推荐规则的结果（消除情况下，随之而来的是已经包含当然，在历史上）。

In any case, the way association rules are used to generate recommendations is that, given some history itemset, we can check each rule's antecedant to see if is contained in the history. If so, then we can recommend the rule's consequent (eliminating cases where the consequent is already contained in the history, of course).

我们可以用不同的指标来排名我们的建议，因为有许多规则，比较它们的历史的时候，我们可能有很多的点击，我们只能做建议的数量有限。一些有用的指标是一个规则的支持的（这是一样的antecedant和consequant的工会的支持），在信心的一个规则（支持该规则在antecedant的支持），而电梯的规则（在antecedant以及由此引发的）等的支持下，该产品的规则的支持的了。

We can use various metrics to rank our recommendations, since with a multitude of rules we may have many hits when comparing them to a history, and we can only make a limited number of recommendations. Some useful metrics are the support of a rule (which is the same as the support of the union of the antecedant and the consequant), the confidence of a rule (the support of the rule over the support of the antecedant), and the lift of a rule (the support of the rule over the product of the support of the antecedant and the consequent), among others.

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