我希望能够将新的“标记行”引入到“随机”显示给用户的数据库中。(这些标记行显示为动画文本的介绍。)
根据这些标语产生的销售数量,我希望好的产品能够涓涓细流,但仍然不那么频繁地展示其他产品。
我可以很容易地提出一个基本的算法,但我想要一些更“统计上准确”的东西。
我真的不知道从哪里开始。我已经做了一段时间,因为我做了比基本统计更多的事情。我的模型需要对公差敏感,但显然它不需要值得PHD。
编辑:我目前正在跟踪“转化率” - 即每个订单点击次数。该值可能最好计算为累积的“所有时间”转换率,并将其输入算法。
发布于 2019-05-10 08:58:24
我建议根据以前的销售情况随机选择加权因子。所以,假设你有这个:
一个简单的加权公式是1 +销售数量,因此这将是选择每个标签的概率:
您可以轻松更改权重公式,以获得所需的分布。
发布于 2019-05-10 09:07:53
这是javascript中的一个例子。并不是说我不建议运行这个客户端......还有很多可以完成的优化。
注意:此处实现createMemberInNormalDistribution()将统一分布转换为正态分布
/*
* an example set of taglines
* hits are sales
* views are times its been shown
*/
var taglines = [
{"tag":"tagline 1","hits":1,"views":234},
{"tag":"tagline 2","hits":5,"views":566},
{"tag":"tagline 3","hits":3,"views":421},
{"tag":"tagline 4","hits":1,"views":120},
{"tag":"tagline 5","hits":7,"views":200}
];
/*set up our stat model for the tags*/
var TagModel = function(set){
var hits, views, sumOfDiff, sumOfSqDiff;
hits = views = sumOfDiff = sumOfSqDiff = 0;
/*find average*/
for (n in set){
hits += set[n].hits;
views += set[n].views;
}
this.avg = hits/views;
/*find standard deviation and variance*/
for (n in set){
var diff =((set[n].hits/set[n].views)-this.avg);
sumOfDiff += diff;
sumOfSqDiff += diff*diff;
}
this.variance = sumOfDiff;
this.std_dev = Math.sqrt(sumOfSqDiff/set.length);
/*return tag to use fChooser determines likelyhood of tag*/
this.getTag = function(fChooser){
var m = this;
set.sort(function(a,b){
return fChooser((a.hits/a.views),(b.hits/b.views), m);
});
return set[0];
};
};
var config = {
"uniformDistribution":function(a,b,model){
return Math.random()*b-Math.random()*a;
},
"normalDistribution":function(a,b,model){
var a1 = createMemberInNormalDistribution(model.avg,model.std_dev)* a;
var b1 = createMemberInNormalDistribution(model.avg,model.std_dev)* b;
return b1-a1;
},
//say weight = 10^n... higher n is the more even the distribution will be.
"weight": .5,
"weightedDistribution":function(a,b,model){
var a1 = createMemberInNormalDistribution(model.avg,model.std_dev*config.weight)* a;
var b1 = createMemberInNormalDistribution(model.avg,model.std_dev*config.weight)* b;
return b1-a1;
}
}
var model = new TagModel(taglines);
//to use
model.getTag(config.uniformDistribution).tag;
//running 10000 times: ({'tagline 4':836, 'tagline 5':7608, 'tagline 1':100, 'tagline 2':924, 'tagline 3':532})
model.getTag(config.normalDistribution).tag;
//running 10000 times: ({'tagline 4':1775, 'tagline 5':3471, 'tagline 1':1273, 'tagline 2':1857, 'tagline 3':1624})
model.getTag(config.weightedDistribution).tag;
//running 10000 times: ({'tagline 4':1514, 'tagline 5':5045, 'tagline 1':577, 'tagline 2':1627, 'tagline 3':1237})
config.weight = 2;
model.getTag(config.weightedDistribution).tag;
//running 10000 times: {'tagline 4':1941, 'tagline 5':2715, 'tagline 1':1559, 'tagline 2':1957, 'tagline 3':1828})
https://stackoverflow.com/questions/-100001142
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