1.让内容与商品实现匹配

谈到跨数据类型推荐，笔者所服务的公司艾克斯智能的量荐推荐引擎早在2016年便上线了此项功能，并成功的在一家专注做女性KOL沙龙的社区“玲珑”中得到了广泛的应用。当时，KOL社区方兴未艾，许多知名的女性KOL在玲珑沙龙分享一些女性生活方式，玲珑沙龙流量很大但苦于没有合适的流量变现方式便找到了我们，我们便提出了通过跨数据类型推荐的方式进行变现。

每天玲珑沙龙发布上千篇文章，例如30岁知性女性该如何化妆、35岁的职场女性该如何穿搭等，跨数据类型推荐通过计算每篇文章的语义匹配与当前文章最相关的N个商品。浏览此文章的用户可直接点击链接进行购买，从内容端完成了商品的消费。要知道，纯人工的方式是无法将每天几千篇的文章挂上与之最匹配的商品。况且商品是有时效性的，而内容大多时效性没有那么强。一篇教穿搭的文章前年有价值，今年仍然有价值，而人工所挂的商品链接可能已早早失效。因此，随着商品上下架、更新并不断帮助文章实时匹配商品的方式，只有跨数据类型推荐。即，不同的数据类型通过语义算法相互匹配。如：根据文章推荐商品、视频，根据视频推荐习题，根据习题推荐问题等。当然，这并不是跨数据类型的全部。

2.小红书的跨数据推荐应用

首先，用户看到感兴趣的内容可从内容标签直接跳转至商品购买页面完成商品的购买。（小红书并没有实现通过机器自动将文章与商品匹配，而是依赖每位种草的达人所挂的链接，我们在浏览小红书的商品链接时，经常会发现失效链接）如图3–2所示。
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图3–2 内容标签直接跳转至商品购买页

其次，小红书不仅可以实现通过浏览内容进入商品购买页面，而且用户在社区的行为数据被机器收集计算后，用户同样可以在商城版块发现自己喜欢的商品。如图3–3所示。

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\* MERGEFORMATINET INCLUDEPICTURE "https://mmbiz.qpic.cn/mmbiz_jpg/YQbWBzEcypLC3N6qJGhhrvWVkVYs5YTYTsKl8DqODaAwgIzrnISoLAp4G7iaht6ueWhgmib2ZejR6kEpTiaOPmjiaQ/640?wx_fmt=jpeg%252525252525252525252525252525252525252525252525252525252525252526wxfrom=5%252525252525252525252525252525252525252525252525252525252525252526wx_lazy=1%252525252525252525252525252525252525252525252525252525252525252526wx_co=1" \* MERGEFORMATINET INCLUDEPICTURE "https://mmbiz.qpic.cn/mmbiz_jpg/YQbWBzEcypLC3N6qJGhhrvWVkVYs5YTYTsKl8DqODaAwgIzrnISoLAp4G7iaht6ueWhgmib2ZejR6kEpTiaOPmjiaQ/640?wx_fmt=jpeg%2525252525252525252525252525252525252525252525252525252525252526wxfrom=5%2525252525252525252525252525252525252525252525252525252525252526wx_lazy=1%2525252525252525252525252525252525252525252525252525252525252526wx_co=1" \* MERGEFORMATINET INCLUDEPICTURE "https://mmbiz.qpic.cn/mmbiz_jpg/YQbWBzEcypLC3N6qJGhhrvWVkVYs5YTYTsKl8DqODaAwgIzrnISoLAp4G7iaht6ueWhgmib2ZejR6kEpTiaOPmjiaQ/640?wx_fmt=jpeg%25252525252525252525252525252525252525252525252525252525252526wxfrom=5%25252525252525252525252525252525252525252525252525252525252526wx_lazy=1%25252525252525252525252525252525252525252525252525252525252526wx_co=1" \* MERGEFORMATINET

图3–3 在商城版块发现自己喜欢的商品

笔者在社区版块对健身相关的内容产生了大量的行为，从而机器通过计算笔者的行为数据，认为笔者可能这段时间比较热衷健身或想去健身，因此推荐了健身相关的装备或器具。即：支持语义模型在所有数据类型中进行推荐，也就是说，用户看了某个商品，那么在文章推荐中也会给你推与这个商品相关的文章。看了某篇文章，我们在给你推商品的时候也会给你推跟这个文章相关的商品，把用户在多数据类型的行为特征打通，实现互通推荐。

3.跨数据推荐的其他用途

当然，跨数据类型推荐的策略不仅适用内容的变现，也可以实现流量的引流和开发新功能。例如，笔者服务过的一家制造业企业的情报系统，因为情报是内部提供和从外部授权得到的，数据湖里的资源并不多，而为了给研发人员提供更多的智力支持，则在情报的相关推荐中推荐相关问答，将浏览情报的流量导流至问答版块，引导用户更多地生产内容。还有像最近上线的网易云音乐的新功能“音乐交友”，根据用户对歌曲的兴趣匹配有同样爱好或者爱好相似的异性，实现兴趣交友。如图3–4所示。

图3–4 网易云音乐新功能“音乐交友”

所以，我们总结一下跨数据类型推荐：

（1）不同数据类型通过语义计算的方式互相匹配、搭配推荐。例如在玲珑沙龙，根据文章推荐商品。

（2）用户在不同数据类型中的行为特征互相打通，互为推荐。例如用户在应用的社区版块浏览了帖子，那么当进入应用的商城版块时，商城中可推荐与用户所浏览的帖子最相关的商品。

值得一提的是，跨数据类型推荐不仅可以实现同应用的推荐，还可以实现APP端、小程序端、手机Wap端、PC端的互通推荐。用户在APP端发生了行为，当打开小程序端时会根据用户在APP端的行为进行小程序端的推荐，实现跨端推荐。当然，前提是各端的用户ID和物料ID是唯一且一致的。

在内容平台中，通过跨数据类型推荐的方式将内容与商品互相连接。同样的一份内容在原有精神消费属性的基础上增加了商品消费属性，原有消费完内容即走的用户看到感兴趣的商品后会有一部分进入商品的转化漏斗中，通过内容为商品进行引流，内容平台也具备了内容电商平台的属性，从而实现了商品销售额的增长。