博客

当前系统的真正问题

(

The Real Issue With the Current Sytems

)

With the rise of e-commerce in this era, a new frontier has opened up. It’s called product recommendations. It’s a no brainers, you recommend the right product to the right customers at the right time and your sales just bump up. As much as it sounds sweet and like a cookie-cutter in reality it’s the complete opposite of that.

随着这个时代电子商务的兴起，一个新的领域开辟了。 这就是产品推荐。 毫不费吹灰之力，您可以在合适的时间向合适的客户推荐合适的产品，而您的销售额才有增长。 尽管听起来很甜美，但实际上却像千篇一律，完全相反。

Product Recommendation is one of the most misdelivered concepts in the practicality of the Data Science industry. There are basically two ways in which the whole industry does that.

在数据科学行业的实用性中，产品推荐是最不正确的概念之一。 整个行业基本上有两种方法可以做到这一点。

• They do it in very basic terms with no tangible return in terms of accuracy. In other words more half of the time, the recommendations are not correct and they don’t sell.

  他们以非常基本的方式做到这一点，在准确性方面没有明显的回报。 换句话说，超过一半的时间，这些建议是不正确的，也不会出售。

• Others with a bigger team and budget do it very well with astounding results and accuracy, meaning the recommendations are actually accurate and it actually does bump up the sales.

  拥有更大团队和预算的其他公司则以惊人的结果和准确性很好地做到了这一点，这意味着建议实际上是准确的，并且确实提高了销售额。

黄金三镖客

(

The Good, The Bad, and The Ugly

)

Now let me introduce you to the concept of the good the bad and the ugly sides of these two methods.

现在，让我向您介绍这两种方法的优缺点和丑陋方面的概念。

The accuracy and reliability of the second one is the good side. The recommendations from this method are accurate and they do make customers buy more and eventually help the sales figure.

第二个方面的准确性和可靠性是好的方面。 这种方法的建议是准确的，确实可以使客户购买更多商品并最终帮助销售。

The bare bone structure and unreliability of the first method is the bad side. Half the times it won’t even recommend anything relevant.

裸露的骨头结构和第一种方法的不可靠性是不利的一面。 一半的时间甚至不建议任何相关内容。

Now let’s come to the ugly one. Just a fun fact; This is my favorite. The efforts and time needed for the second one is the ugly side, perhaps the ugliest side. Just for the reference, it’s the equivalent of the way the valve’s half-life team develops. It will come out eventually at some point but no one knows how long it will take. Spoiler alert half-life 4 is confirmed, fingers crossed.

现在让我们来看看丑陋的。 只是一个有趣的事实； 这是我最喜欢的。 第二个方面需要付出的努力和时间是丑陋的一面，也许是最丑陋的一面。 仅供参考，它等同于阀门半衰期团队的发展方式。 它最终会在某个时候问世，但没人知道它需要多长时间。 扰流板警报半衰期4得到确认，手指交叉。

丑陋不是好事还是坏事

(

Not the Good nor the Bad Just the Ugly

)

The industry standard process is quite frankly a little bit of a long road. Don’t get me wrong they do work but takes a humongous amount of time. The processes are as follows:

坦率地说，行业标准流程还有很长的路要走。 不要误会我的意思，他们确实可以工作，但是要花费大量的时间。 流程如下：

• Collect the data then clean the data.

  收集数据，然后清理数据。

• Do some of the feature engineerings to get the most out of the data.

  进行一些功能设计，以充分利用数据。

• Train a bunch of machine learning models to figure out which one is slightly better than the other ones.

  训练一堆机器学习模型，以确定哪个模型比另一个模型稍好。

• Validate the models to adjust and tune the selected model.

  验证模型以调整和调整所选模型。

• Test the model to justify the real-world implementation of the model.

  测试模型以证明模型的实际实现。

There might be only 5 bullet points, but looks can be very deceiving. Specifically the second one. That’s the one that will eat up your weeks of work time for its lunchtime and let’s not even talk about the third one. This third one is the proof that third time is not always the charm. In a nutshell, to come up with a recommendation system of this scale can take up to months and not to mention the budgetary factors. Not every e-commerce company has that amount of budget and manpower to pull off such a task.

可能只有5个要点，但外观可能非常具有欺骗性。 特别是第二个。 那是一个会花费您数周的工作时间作为午餐时间的时间，而我们甚至不谈论第三个时间。 这第三次证明了第三次并不总是魅力。 简而言之，提出这样一个规模的推荐系统可能需要长达数月的时间，更不用说预算因素了。 并非每个电子商务公司都拥有完成这项任务所需的预算和人力。

向我的小朋友打个招呼：节省您的时间。

(

Say Hello To My Little Friend Called: Save your time.

)

Well, the name of my little friend is actually


Enhencer


. It’s a product recommendation tool that tries to be:

the good the better and the beautiful

. The good is the fact that it’s very fast, the better that its super easy, and lastly it’s very intuitive dashboard making it all but beautiful. It tries to solve the issue of the complexity and the budgetary that exist today. It has some primary features and which translates to; save your time, save your time and say goodbye to complexity.

好吧，我的小朋友的名字实际上是


Enhencer


。 这是一个试图成为的产品推荐工具：

越好越好

。 好处是它的速度非常快，超级容易的效果也更好，最后它是非常直观的仪表板，使它变得非常漂亮。 它试图解决当今存在的复杂性和预算问题。 它具有一些主要功能，可以转换为； 节省您的时间，节省时间，并告别复杂性。

Well, the real features are:

好吧，真正的功能是：

• 
  It handles feature engineering automatically.
  

  
  它自动处理要素工程。
  

• 
  It trains a bunch of machine learning models automatically.
  

  
  它会自动训练一堆机器学习模型。
  

• 
  It compares and chooses the best one automatically.
  

  
  它会自动比较并选择最佳的一个。
  

• 
  It validates the models automatically.
  

  
  它会自动验证模型。
  

The whole process can be summed as: Enhancers require the users to upload the Sales, Customers, and Products data. After all the data are in the platforms Enhencer literally handles all the feature engineering, model training, and all on its own automatically. It uses a machine-learning algorithm behind the curtain to achieve this. This also means the users don’t have to know any data Science knowledge let alone write a single line of code.

整个过程可以总结为：增强程序要求用户上载Sales，Customers和Products数据。 将所有数据存储在平台中后，Enhencer可以自动处理所有的功能工程，模型训练以及所有这些工作。 它使用幕后的机器学习算法来实现这一目标。 这也意味着用户不必知道任何数据科学知识，更不用说编写一行代码了。

In the end, Enhencer provides the Product Recommendations for each customer and each product category in a dashboard like this. The dashboard is just a representation of model outputs and translated into daily life dashboards that anyone can understand without having any prior data Science and technical knowledge. You can either download the lists in static file format or implement the system to your live e-commerce site with API. This makes the implementation real easy.

最后，Enhencer在这样的仪表板中为每个客户和每个产品类别提供产品推荐。 仪表板只是模型输出的一种表示形式，已转换为任何人都可以理解的日常生活仪表板，而无需任何事先数据科学和技术知识。 您可以以静态文件格式下载列表，也可以使用API​​将系统实施到实时电子商务站点。 这使得实现起来非常容易。

The biggest conservative thoughts you might be having right now;

Is it accurate?

您现在可能会想到的最大的保守主义想法；

准确吗？

Since most of the things are done automatically by a machine learning platform, one can definitely raise such a question. Enhencer generated Recommendations are very precise accurate when it comes to real-world results and generating actual sales.

The biggest reason is Enhencer takes customers’ past purchasing behaviors and website visit data into account simultaneously before recommending any product.

One other thing Enhencer takes into account that others fail is it takes customers’ budget and purchasing power into account.

由于大多数事情都是由机器学习平台自动完成的，因此无疑可以提出这样一个问题。 当涉及到实际结果并产生实际销售时，由Enhencer生成的建议非常精确。

最大的原因是，在推荐任何产品之前，Enhencer会同时考虑客户过去的购买行为和网站访问数据。

Enhencer考虑到其他人失败的另一件事是，它考虑了客户的预算和购买力。

What this means is; If a customer wants to buy a phone and searches for some on the website it’s not enough to just recommend some phones.

If the customer is on a budget and if you recommend an expensive phone to them just because it’s the most popular one right now then the recommendation is very likely to go to waste.


Enhencer tackles this by combining both past purchase behavior thus considering purchasing power and website visits together to recommend the relevant product from the product category to maximize the possibility of a new sale.

这意味着什么； 如果客户想购买一部电话并在网站上搜索一些电话，仅推荐一些电话是不够的。

如果客户预算有限，并且仅因为它是目前最受欢迎的电话而向他们推荐昂贵的电话，那么该推荐很可能会浪费掉。


Enhencer通过结合过去的购买行为，考虑购买力和网站访问来解决这一问题，从而从产品类别中推荐相关产品，以最大程度地进行新的销售。

A lot of large scales e-commerce sites have started using it on their system. You can find the whole list on their website

https://enhencer.com/

.

许多大型电子商务站点已开始在其系统上使用它。 您可以在其网站

https://enhencer.com/

上找到整个列表。

Enhencer is just one step in the right way. It’s super accessible and easy to use. It brings down the time required for such a process from weeks/months to just a few mere minutes. This means smaller e-commerce to larger ones all can obtain relevant and accurate predictions very fast and most importantly very easily.

增强只是正确方法的一步。 它超级易于访问且易于使用。 它将这种过程所需的时间从数周/数月缩短到仅几分钟。 这意味着从小型电子商务到大型电子商务，都可以非常快速且最重要的是非常容易地获得相关且准确的预测。

The Official Website:



https://enhencer.com/

官方网站：



https



：



//enhencer.com/

Product Recommendation Case:



https://enhencer.com/product-recommendation

产品推荐案例：



https



：



//enhencer.com/product-recommendation