????雖然這些應用每一款都算不錯，但是它不能取代一個熟悉你、也熟悉你愛好的人，因為還不算是突破性的進展。無論是對于這三家創業公司，還是對于很多正在開發推薦引擎的公司來說，更緊迫的問題是，這些公司必須擴展自己的能力，才能實現里斯為Ness設定的目標，也就是“在人們尋找下一個他們可能喜歡的事物時，充當他們可信的信息來源”。這意味著Ness必須要涵蓋更多的東西，而不僅僅是餐廳。

????里斯表示，利用Ness現有的技術，Ness還可以用來推薦書籍、電影、旅行目的地或是夜生活場所。約翰遜認為對于Zite來說也是一樣。他同時指出，開發一個全方位的推薦引擎將面臨一個難以避免的挑戰，也就是要完善一個“社交圖譜里的Google”。或許最終的產品將成為谷歌的一個強力競爭對手，它可能通過兩種方式提供人們要尋找的一東西，一是嚴格根據人們的搜索歷史，二是根據人們的個人交往情況進行推薦。但是要注意，這兩者都基于一點，也就是所謂的大數據。

????但是我們真的想要這種東西嗎？臉譜（Facebook現在想做的就是統一整個網絡，Facebook原本是做社交起家，然后成了你瘋狂貼照片的地方，現在又包括了即時通訊、地理位置服務、社交游戲和一個商業市場?，F在我們還不知道，這種“大一統”是否會讓用戶買賬，抑或會令有些用戶感到心煩，甚至導致流失用戶

????許多貪心的網絡用戶喜歡使用不同的應用來實現不同的功能。比如我喜歡用Kickstarter向一些很酷的社區項目提供資金，用Twitter發布新聞，用Facebook進行個人分享，用Instagram發布照片。我相信這些應用在各自的領域都是完美的，我也不想要一個“一站式的服務”。更重要的是，許多社交應用，比如Kickstarter和Tumblr等之所以吸引人，并不是應為他們嘗試著去懂你，而是它們的用戶喜歡把自己的興趣投射到這個平臺上。

????亞馬遜(Amazon)和Netflix在預測技術上還停留在“1.0”時代（約翰遜把這兩家公司稱為“老經典”），但它們仍然非常成功，而且可能很難打敗。亞馬遜的推薦引擎依賴于一個基本公式，它向你推薦的產品基于你的瀏覽史、購物史，并且與其他顧客購買的產品進行關聯。這個模式是成功的。Netflix采用的也是簡單有效的法子，隨著你選擇電影的時間越來越長，Netflix會變得越來越聰明。如果說這幾個科技巨頭在預測技術領域都做得不錯，那么像Spotify或Ness這樣的公司，如果指望單純靠增加幾個細分領域就能獲得成功，恐怕很難。

????目前來說，我還是依靠我自己的人肉推薦引擎吧。因為我的朋友和家人比任何一款應用軟件都更加了解我。至于我是否了解我自己，這個問題就留給谷歌（Google）、Facebook、蘋果（Apple），以及大量關于你和我的數據吧。（財富中文網）

????譯者：樸成奎

????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.