我的应用不懂我

    虽然这些应用每一款都算不错，但是它不能取代一个熟悉你、也熟悉你爱好的人，因为还不算是突破性的进展。无论是对于这三家创业公司，还是对于很多正在开发推荐引擎的公司来说，更紧迫的问题是，这些公司必须扩展自己的能力，才能实现里斯为Ness设定的目标，也就是“在人们寻找下一个他们可能喜欢的事物时，充当他们可信的信息来源”。这意味着Ness必须要涵盖更多的东西，而不仅仅是餐厅。

    里斯表示，利用Ness现有的技术，Ness还可以用来推荐书籍、电影、旅行目的地或是夜生活场所。约翰逊认为对于Zite来说也是一样。他同时指出，开发一个全方位的推荐引擎将面临一个难以避免的挑战，也就是要完善一个“社交图谱里的Google”。或许最终的产品将成为谷歌的一个强力竞争对手，它可能通过两种方式提供人们要寻找的一东西，一是严格根据人们的搜索历史，二是根据人们的个人交往情况进行推荐。但是要注意，这两者都基于一点，也就是所谓的大数据。

    但是我们真的想要这种东西吗？脸谱（Facebook现在想做的就是统一整个网络，Facebook原本是做社交起家，然后成了你疯狂贴照片的地方，现在又包括了即时通讯、地理位置服务、社交游戏和一个商业市场。现在我们还不知道，这种“大一统”是否会让用户买账，抑或会令有些用户感到心烦，甚至导致流失用户

    许多贪心的网络用户喜欢使用不同的应用来实现不同的功能。比如我喜欢用Kickstarter向一些很酷的社区项目提供资金，用Twitter发布新闻，用Facebook进行个人分享，用Instagram发布照片。我相信这些应用在各自的领域都是完美的，我也不想要一个“一站式的服务”。更重要的是，许多社交应用，比如Kickstarter和Tumblr等之所以吸引人，并不是应为他们尝试着去懂你，而是它们的用户喜欢把自己的兴趣投射到这个平台上。

    亚马逊(Amazon)和Netflix在预测技术上还停留在“1.0”时代（约翰逊把这两家公司称为“老经典”），但它们仍然非常成功，而且可能很难打败。亚马逊的推荐引擎依赖于一个基本公式，它向你推荐的产品基于你的浏览史、购物史，并且与其他顾客购买的产品进行关联。这个模式是成功的。Netflix采用的也是简单有效的法子，随着你选择电影的时间越来越长，Netflix会变得越来越聪明。如果说这几个科技巨头在预测技术领域都做得不错，那么像Spotify或Ness这样的公司，如果指望单纯靠增加几个细分领域就能获得成功，恐怕很难。

    目前来说，我还是依靠我自己的人肉推荐引擎吧。因为我的朋友和家人比任何一款应用软件都更加了解我。至于我是否了解我自己，这个问题就留给谷歌（Google）、Facebook、苹果（Apple），以及大量关于你和我的数据吧。（财富中文网）

    译者：朴成奎

    That each app does a decent job, but cannot replace the usefulness of a live person familiar with you and your likes, is no breakthrough epiphany. The more pressing question may be which startup -- from these three or from the myriad others already out there -- will end up expanding its repertoire to achieve what Reese says is his mission with Ness: "become that trusted source for people to find out the next thing they'll like." That sounds like it would encompass a lot more than restaurants.

    Reese says Ness could just as easily use its technology to recommend books, movies, travel destinations, or nightlife activities. Then again, Johnson believes the same of Zite. He also posits that coming up with an all-in-one recommendation engine will be inextricably linked to the challenge of perfecting a "Google for the social graph." Perhaps the final product will be a Google (GOOG) rival that offers what you're seeking in two forms: one based strictly on your own search history, the other inspired by your personal connections. Take note: It will all rely on big data.

    But would we want such a thing? Consolidating the Web is exactly what Facebook (FB) is trying to do; what began as a place for checking people out eventually became your photo dumping grounds and now includes instant messaging, location services, social games, and a commercial marketplace. It remains to be seen whether all of this is annoying enough to cost them users or if people will just give in.

    Many avid Internet users prefer to have their various functions in separate silos. I likeKickstarter for funding cool community projects; Twitter for breaking news; Facebook for more personal sharing; and Instagram for photos. I trust each of these entities for the activity it's perfected. I wouldn't want a one-stop shop. Moreover, many of these outlets, like multifaceted Kickstarter as well as, say, Tumblr, are appealing precisely because they do not attempt to know you; instead, their users tend to project their own interests onto the platform.

    Both Amazon (AMZN) and Netflix (NFLX) (Johnson calls these "the old classics"), which are akin to the "1.0" of predictive technology, still work pretty well and may be hard to beat. Amazon's recommendation engine relies on a basic formula (despite the highfalutin term they've given it, "item-to-item collaborative filtering") that suggests products to you based on your viewing history, your purchase history, and which related products other customers bought. And it works. The same goes for Netflix, which, as you spend more time choosing movies, becomes quite smart indeed. If these giants of the tell-me-what-to-try space are doing just fine, it may be tough for a Spotify or Ness to simply add more verticals and hit the gas pedal.

    For now, I'll rely on my own human recommendation engine, thanks. My friends and family know me better than any one app ever could. Whether I know myself, well, that's probably a question for Google, Facebook, and Apple (AAPL), and their vast piles of data on me -- and you.