如何在亚马逊的推荐功能工作？ [英] How does the Amazon Recommendation feature work?

问题描述

去什么技术在亚马逊推荐技术的屏幕后面？我认为，亚马逊的建议是目前最好的市场，但他们如何为我们提供了这样的相关建议？

最近，我们已经参与了类似的建议类型的项目，但一定会想知道亚马逊的推荐技术，从技术角度看，在和输出。

任何投入将是非常美联社preciated。

更新：

这<一href="http://www.google.com/patents?id=dtp6AAAAEBAJ&printsec=abstract&zoom=4&source=gbs_overview_r&cad=0#v=onepage&q=&f=false">patent介绍了如何个性化建议这样做，但它不是非常技术性的，所以这将是非常好的，如果一些见解可以提供。

这是大卫，亲和分析形成的基础，这种类型的推荐引擎的意见。而且，这里有关于这个专题的一些好读

1. 揭秘购物篮分析
2. 购物篮分析
3. 亲和分析

推荐阅读：

1. 数据挖掘：概念与技术

解决方案

这既是一门艺术，一门科学。研究的典型领域围绕市场购物篮分析（也称为亲和分析），这是数据挖掘领域的一个子集。在这样的系统中的典型组件包括标识主驾驶员项和的亲和力的物品的识别（配件加售，交叉销售）。

请记住数据源也得我的......

1. 在购买购物车=真钱从真实的人花费在实际项目=强大的数据和它的很多。
2. 产品添加到车，但放弃了。
3. 定价实验网上（A / B测试等），在那里他们提供不同的价格相同的产品，并查看结果
4. 在包装的实验（A / B测试，等等），他们提供不同的产品在不同的捆绑或项目折价各配对
5. 心愿 - 什么是他们专门为你 - 合计也可以同样处理，购物篮分析的另一个数据流
6. 在推介网站（身份证，你就从会提示其他感兴趣的项目）
7. （多久，然后单击后退并选择不同的项目）驻留时间
8. 将您或那些在你的社交网络分级/买入圈 - 如果你率你喜欢你得到更多你喜欢的事情，如果你确认了我已经拥有它的东西按钮，他们创造了你一个非常完整的个人资料
9. 在人口统计信息（您的收货地址等） - 他们知道什么是流行在一般地区为你的孩子，你自己，你的配偶等。
10. 在用户细分=你的孩子买3本书在不同的月份？可能有孩子或者更多......等。
11. 在直复营销的点击数据 - 你从他们那里得到了电子邮件，通过点击？他们知道这是你通过点击什么的电子邮件，以及是否你买了它作为一个结果。
12. 单击会话路径 - 你查看，无论是否你的购物车中去什么
13. 的次数最终购买之前查看了项目
14. 如果您正在处理一个实体商店，他们可能有你的身体的购买记录熄灭，以及（如玩具反斗城或东西是在网上，也有实体店）
15. 等。等等，等等

幸运的是，人们的行为同样在总让他们更了解购买人群的大，他们知道什么会和不会出售，并与每一笔交易，每一个等级/收藏加入/浏览他们知道如何更亲自量身越好建议。请记住，这是很有可能的全套东西在建议最终影响的只是一个小样本等。

现在我对亚马逊如何做业务（从来没有在那里工作），所有我做的是在谈论经典方法网上商务的问题，没有任何内幕消息 - 我曾经是谁的工作数据挖掘和分析的PM对于微软的产品被称为商业服务器。我们发货的Commerce Server，允许人们建立网站，具有类似功能的工具....但更大的销量数据好更好的模型 - 和亚马逊大。我只能想象它是多么的乐趣，在一个商业驱动的网站那么多的数据模型玩。现在很多的算法（如predictor是开始了在商业服务器）已经转移直接内的微软SQL 。

四大取一个-方法，你应该是：

1. 在亚马逊（或零售商）正在寻求汇总数据的交易吨人和万吨......这使得他们甚至建议pretty的同时为他们的网站上匿名用户。
2. 在亚马逊（或任何复杂的零售商）的跟踪行为，是记录在采购人的使用，为进一步完善对海量汇总数据之上。
3. 经常有手段在骑积累的数据，并以社论的建议，控制的具体线产品经理（像一些人谁拥有数码相机垂直或爱情小说垂直或类似）他们真的是专家
4. 经常有促销优惠（即索尼和松下或尼康或佳能或冲刺或Verizon公司支付额外的钱给零售商，或者给在大批量或其他东西在那些线条更好的折扣），这将造成一定的建议上升到顶部往往比其他人 - 背后总有这样一些合理的业务逻辑和业务理由针对每笔交易赚更多或降低批发成本，等等。

在实际执行方面？几乎所有的大型在线系统归结到某一组管道（或过滤模式的实现或工作流程等，你叫什么你会），允许为背景的一系列模块，可以采用某种形式的待评估业务逻辑。

通常，不同的管道将与页面上的每个单独的任务关联 - 你可能有一个，它推荐的套餐/加售（即你正在看的项目购买此），做替代品，一个（即买，而不是你要找的东西这一点），拉从你的愿望清单（按产品类别或类似的）最密切相关的项目和其他。

这些管线的结果能够被放置在页面的不同部分（上面的滚动条，滚动下面，左边，右边，不同的字体，不同大小的图像，等等）和测试，以看看哪个效果最好。由于您使用漂亮的易于即插即用定义的业务逻辑，这些管线你结束了乐高积木的道德等价物，可以轻松挑选和你想的时候，你建一个管道应用的业务逻辑选择模块它允许更快的创新，更多的试验，并最终获得更高的利润。

难道这帮助呢？希望，让您深入了解一点点是如何工作的，一般用于几乎任何电子商务网站 - 不只是亚马逊。亚马逊（从谈话已经在那里工作的朋友）的驱动非常的数据和连续测量它的有效性的用户体验和定价，促销，包装等 - 他们是一个非常复杂的零售商在网上，并有可能在前沿很多他们使用，以优化利润的算法 - 那些有可能其秘密（你知道像公式肯德基的秘制香料）和guaarded这样

What technology goes in behind the screens of Amazon recommendation technology? I believe that Amazon recommendation is currently the best in the market, but how do they provide us with such relevant recommendations?

Recently, we have been involved with similar recommendation kind of project, but would surely like to know about the in and outs of the Amazon recommendation technology from a technical standpoint.

Any inputs would be highly appreciated.

Update:

This patent explains how personalized recommendations are done but it is not very technical, and so it would be really nice if some insights could be provided.

From the comments of Dave, Affinity Analysis forms the basis for such kind of Recommendation Engines. Also here are some good reads on the Topic

1. Demystifying Market Basket Analysis
2. Market Basket Analysis
3. Affinity Analysis

Suggested Reading:

1. Data Mining: Concepts and Technique

解决方案

It is both an art and a science. Typical fields of study revolve around market basket analysis (also called affinity analysis) which is a subset of the field of data mining. Typical components in such a system include identification of primary driver items and the identification of affinity items (accessory upsell, cross sell).

Keep in mind the data sources they have to mine...

1. Purchased shopping carts = real money from real people spent on real items = powerful data and a lot of it.
2. Items added to carts but abandoned.
3. Pricing experiments online (A/B testing, etc.) where they offer the same products at different prices and see the results
4. Packaging experiments (A/B testing, etc.) where they offer different products in different "bundles" or discount various pairings of items
5. Wishlists - what's on them specifically for you - and in aggregate it can be treated similarly to another stream of basket analysis data
6. Referral sites (identification of where you came in from can hint other items of interest)
7. Dwell times (how long before you click back and pick a different item)
8. Ratings by you or those in your social network/buying circles - if you rate things you like you get more of what you like and if you confirm with the "i already own it" button they create a very complete profile of you
9. Demographic information (your shipping address, etc.) - they know what is popular in your general area for your kids, yourself, your spouse, etc.
10. user segmentation = did you buy 3 books in separate months for a toddler? likely have a kid or more.. etc.
11. Direct marketing click through data - did you get an email from them and click through? They know which email it was and what you clicked through on and whether you bought it as a result.
12. Click paths in session - what did you view regardless of whether it went in your cart
13. Number of times viewed an item before final purchase
14. If you're dealing with a brick and mortar store they might have your physical purchase history to go off of as well (i.e. toys r us or something that is online and also a physical store)
15. etc. etc. etc.

Luckily people behave similarly in aggregate so the more they know about the buying population at large the better they know what will and won't sell and with every transaction and every rating/wishlist add/browse they know how to more personally tailor recommendations. Keep in mind this is likely only a small sample of the full set of influences of what ends up in recommendations, etc.

Now I have no inside knowledge of how Amazon does business (never worked there) and all I'm doing is talking about classical approaches to the problem of online commerce - I used to be the PM who worked on data mining and analytics for the Microsoft product called Commerce Server. We shipped in Commerce Server the tools that allowed people to build sites with similar capabilities.... but the bigger the sales volume the better the data the better the model - and Amazon is BIG. I can only imagine how fun it is to play with models with that much data in a commerce driven site. Now many of those algorithms (like the predictor that started out in commerce server) have moved on to live directly within Microsoft SQL.

The four big take-a-ways you should have are:

1. Amazon (or any retailer) is looking at aggregate data for tons of transactions and tons of people... this allows them to even recommend pretty well for anonymous users on their site.
2. Amazon (or any sophisticated retailer) is keeping track of behavior and purchases of anyone that is logged in and using that to further refine on top of the mass aggregate data.
3. Often there is a means of over riding the accumulated data and taking "editorial" control of suggestions for product managers of specific lines (like some person who owns the 'digital cameras' vertical or the 'romance novels' vertical or similar) where they truly are experts
4. There are often promotional deals (i.e. sony or panasonic or nikon or canon or sprint or verizon pays additional money to the retailer, or gives a better discount at larger quantities or other things in those lines) that will cause certain "suggestions" to rise to the top more often than others - there is always some reasonable business logic and business reason behind this targeted at making more on each transaction or reducing wholesale costs, etc.

In terms of actual implementation? Just about all large online systems boil down to some set of pipelines (or a filter pattern implementation or a workflow, etc. you call it what you will) that allow for a context to be evaluated by a series of modules that apply some form of business logic.

Typically a different pipeline would be associated with each separate task on the page - you might have one that does recommended "packages/upsells" (i.e. buy this with the item you're looking at) and one that does "alternatives" (i.e. buy this instead of the thing you're looking at) and another that pulls items most closely related from your wish list (by product category or similar).

The results of these pipelines are able to be placed on various parts of the page (above the scroll bar, below the scroll, on the left, on the right, different fonts, different size images, etc.) and tested to see which perform best. Since you're using nice easy to plug and play modules that define the business logic for these pipelines you end up with the moral equivalent of lego blocks that make it easy to pick and choose from the business logic you want applied when you build another pipeline which allows faster innovation, more experimentation, and in the end higher profits.

Did that help at all? Hope that give you a little bit of insight how this works in general for just about any ecommerce site - not just Amazon. Amazon (from talking to friends that have worked there) is very data driven and continually measures the effectiveness of it's user experience and the pricing, promotion, packaging, etc. - they are a very sophisticated retailer online and are likely at the leading edge of a lot of the algorithms they use to optimize profit - and those are likely proprietary secrets (you know like the formula to KFC's secret spices) and guaarded as such.

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