我的应用不懂我

    随着年纪渐长，工作越来越忙，我们越来越难主动发现生活中美好的事物，随之也涌现出了很多自称了解每个用户的需求，能够帮你推荐喜欢的音乐、餐厅或杂志文章的应用软件。

    最近我和我大学的好朋友去了一趟华盛顿特区，这位朋友现在是一名厨师。说起来有点不好意思，这还是我第一次在八年级以后去华盛顿。我对这个城市一无所知，因此对我的帮助越多越好。我把笔记本电脑放在了家里，整整两天时间完全依赖移动设备，也就是我的iPhone和iPad（我们还第一次尝试了Airbnb）。

    在选餐厅的问题上，我依靠的是Ness。今年年方27岁的科里•里斯于2009年与人共同创办了Ness计算公司。这款应用有一个“相似度分数”，可以表示出你有多大的可能会喜欢某个推荐。里斯表示，Ness最终可能会成为一个个性化的搜索引擎，但是现在这个应用主要还是针对餐厅和咖啡厅。他不无自豪地说，用户们总是告诉他：“我觉得Ness很懂我。”新闻阅读器Zite的CEO、34岁的马克•约翰逊也说，Zite的用户们都表示：“Zite很懂我。”科技界中有不少精英人才都在搞推荐引擎，这一点也不值得奇怪。里斯说：“我认为，直接输入‘我应该和朋友在哪吃饭’或‘附近有什么很酷的商店’，这个概念已经开始在移动设备上成为现实了，就算是在户外也可以实现。”

    它的工作原理是什么呢？当你第一次打开Ness，它要让你按照五个档次，给当前位置附近的10家餐厅打分。我去华盛顿前，在纽约的曼哈顿完成了打分的过程，不过我发现这个过程是有缺陷的，因为它没有拉开菜系的档次。比如它把米其林三星餐厅老板丹尼尔•布鲁德的DBGB高档餐厅和汉堡王（Burger King）放在同一个屏幕里让人打分，同时这些餐厅里还包括了星巴克（Starbucks）。同时，在你给酒吧打分的时候，它列出的有些酒吧里也提供食物。比如说我喜欢一家叫Brother Jimmy’s的酒吧，是因为我喜欢它有往啤酒杯里扔乒乓球的游戏。他们的鸡翅还有可以，不过如果我给打它了四星，Ness会不会开始经常向我推荐其它弥漫着兄弟会作风的酒吧？不过自从我到了华盛顿之后，Ness的表现要好了一些。根据我在纽约打的分，它向我推荐了一些地中海风情的餐厅，一些中东风味，以及几家我的朋友慕名已久的高端美式餐厅。除了按照你可能喜欢的程度排名之外，Ness还按就餐价格列出了一张排名，好让你知道该进哪一家。同时它也会告诉你，某家餐厅是不是城里第一、第二、第三火爆的这种类型的餐厅。（比如它推荐的José Andrés' Zaytinya就是华盛顿最火爆的地中海风味餐厅。）我们最后选择了一家名叫Central Michel Richard的餐厅（Ness称我们喜欢它的可能性有82%），我们果然美美地吃了一顿。

    吃完午饭后，我们在通过Airbnb租来的公寓房间里连上了Wi-Fi，然后我花了一点时间在Zite上看杂志，Zite是一款像Flipboard一样的所谓“智能杂志”应用。虽然Flipboard在二者间的名气更大，但Zite似乎能更好地了解用户的阅读习惯，哪怕你不把它绑定你的社交媒体也是一样。我已经用Zite几个星期了，而且我发现，我“顶”或“踩”的报道越多，它向我的个人页面推荐的文章就越符合我的品味。

    As you grow older and busier, it becomes more difficult to make spontaneous discoveries. Or at least that's the theory behind a bevy of so-called predictive apps purporting to know each user well enough to hand them their next favorite song, restaurant, or magazine article.

    I gave these tools a test run on a recent trip to D.C. with my best friend from college, who is now a chef. Embarrassingly, it was my first visit to D.C. since the eighth grade; I knew nothing about the city and needed all the help I could get. I left the laptop at home and went strictly mobile for two days, bringing only my iPhone and iPad. (We also tried Airbnb for the first time.)

    For restaurant ideas, I turned to Ness. Corey Reese, 27, co-founded Ness Computing in 2009. The app produces a "likeness score," a percentage that denotes how likely you are to like a particular recommendation. Reese says that Ness could eventually become a personalized search engine, but for now the venture is focusing on restaurants and cafes. He brags that users keep telling him, "It feels like Ness knows me." Mark Johnson, the 34-year-old CEO of newsreaderZite, also says that his app's users rave: "My Zite knows me." It should come as no surprise that more than a few smart people in tech are working on recommendation engines. "We think your entry point for 'Where should I eat with my friends' or 'What's the cool store nearby' is happening on mobile now," says Reese. "It's happening when you're already out and about."

    How does it work? When you first open Ness it asks you to rate, on a five-star scale, 10 restaurants near your current location. I did this in Manhattan before heading to D.C. and found the process flawed. Because it doesn't distinguish between levels of cuisine, it will ask you to rateDaniel Boulud's pricey DBGB in the same screen as it asks you to rate Burger King (BKW). It also includes Starbucks (SBUX). Similarly, it asks you to rate bars that happen to serve food. Sure, I like Brother Jimmy's -- for playing beer pong. Their wings are okay, but if I give it four stars, will Ness start offering me frat bars regularly?

    Once in D.C., Ness fared better. Based on my NYC ratings, it offered us a Mediterranean place, some Middle Eastern fare, and a few upscale American restaurants my friend already knew about. Ness includes, along with its percentage prediction, a price rating so you know what you're getting into. It'll also tell you if a place is the first, second, or third most popular restaurant of its type in the city. (José Andrés' Zaytinya, which it offered, was the most popular Mediterranean in Washington.) We chose Central Michel Richard (Ness promised an 82%) and enjoyed our meal.

    After lunch, connected to Wi-Fi in the apartment we had rented on Airbnb, I spent some time with Zite, a so-called "intelligent magazine" a la Flipboard. Though Flipboard has been the buzzier of the two, Zite seems to learn its user's reading habits better than Flipboard, even if you choose not to connect it to your social media. I had been using Zite for a few weeks and, indeed, found that the more stories and articles to which I gave thumbs up or down, the better it was getting with the stories it displayed on my personalized front page.