????隨著年紀(jì)漸長(zhǎng)，工作越來(lái)越忙，我們?cè)絹?lái)越難主動(dòng)發(fā)現(xiàn)生活中美好的事物，隨之也涌現(xiàn)出了很多自稱了解每個(gè)用戶的需求，能夠幫你推薦喜歡的音樂(lè)、餐廳或雜志文章的應(yīng)用軟件。

????最近我和我大學(xué)的好朋友去了一趟華盛頓特區(qū)，這位朋友現(xiàn)在是一名廚師。說(shuō)起來(lái)有點(diǎn)不好意思，這還是我第一次在八年級(jí)以后去華盛頓。我對(duì)這個(gè)城市一無(wú)所知，因此對(duì)我的幫助越多越好。我把筆記本電腦放在了家里，整整兩天時(shí)間完全依賴移動(dòng)設(shè)備，也就是我的iPhone和iPad（我們還第一次嘗試了Airbnb）。

????在選餐廳的問(wèn)題上，我依靠的是Ness。今年年方27歲的科里?里斯于2009年與人共同創(chuàng)辦了Ness計(jì)算公司。這款應(yīng)用有一個(gè)“相似度分?jǐn)?shù)”，可以表示出你有多大的可能會(huì)喜歡某個(gè)推薦。里斯表示，Ness最終可能會(huì)成為一個(gè)個(gè)性化的搜索引擎，但是現(xiàn)在這個(gè)應(yīng)用主要還是針對(duì)餐廳和咖啡廳。他不無(wú)自豪地說(shuō)，用戶們總是告訴他：“我覺(jué)得Ness很懂我。”新聞閱讀器Zite的CEO、34歲的馬克?約翰遜也說(shuō)，Zite的用戶們都表示：“Zite很懂我。”科技界中有不少精英人才都在搞推薦引擎，這一點(diǎn)也不值得奇怪。里斯說(shuō)：“我認(rèn)為，直接輸入‘我應(yīng)該和朋友在哪吃飯’或‘附近有什么很酷的商店’，這個(gè)概念已經(jīng)開(kāi)始在移動(dòng)設(shè)備上成為現(xiàn)實(shí)了，就算是在戶外也可以實(shí)現(xiàn)。”

????它的工作原理是什么呢？當(dāng)你第一次打開(kāi)Ness，它要讓你按照五個(gè)檔次，給當(dāng)前位置附近的10家餐廳打分。我去華盛頓前，在紐約的曼哈頓完成了打分的過(guò)程，不過(guò)我發(fā)現(xiàn)這個(gè)過(guò)程是有缺陷的，因?yàn)樗鼪](méi)有拉開(kāi)菜系的檔次。比如它把米其林三星餐廳老板丹尼爾?布魯?shù)碌腄BGB高檔餐廳和漢堡王（Burger King）放在同一個(gè)屏幕里讓人打分，同時(shí)這些餐廳里還包括了星巴克（Starbucks）。同時(shí)，在你給酒吧打分的時(shí)候，它列出的有些酒吧里也提供食物。比如說(shuō)我喜歡一家叫Brother Jimmy’s的酒吧，是因?yàn)槲蚁矚g它有往啤酒杯里扔乒乓球的游戲。他們的雞翅還有可以，不過(guò)如果我給打它了四星，Ness會(huì)不會(huì)開(kāi)始經(jīng)常向我推薦其它彌漫著兄弟會(huì)作風(fēng)的酒吧？不過(guò)自從我到了華盛頓之后，Ness的表現(xiàn)要好了一些。根據(jù)我在紐約打的分，它向我推薦了一些地中海風(fēng)情的餐廳，一些中東風(fēng)味，以及幾家我的朋友慕名已久的高端美式餐廳。除了按照你可能喜歡的程度排名之外，Ness還按就餐價(jià)格列出了一張排名，好讓你知道該進(jìn)哪一家。同時(shí)它也會(huì)告訴你，某家餐廳是不是城里第一、第二、第三火爆的這種類型的餐廳。（比如它推薦的José Andrés' Zaytinya就是華盛頓最火爆的地中海風(fēng)味餐廳。）我們最后選擇了一家名叫Central Michel Richard的餐廳（Ness稱我們喜歡它的可能性有82%），我們果然美美地吃了一頓。

????吃完午飯后，我們?cè)谕ㄟ^(guò)Airbnb租來(lái)的公寓房間里連上了Wi-Fi，然后我花了一點(diǎn)時(shí)間在Zite上看雜志，Zite是一款像Flipboard一樣的所謂“智能雜志”應(yīng)用。雖然Flipboard在二者間的名氣更大，但Zite似乎能更好地了解用戶的閱讀習(xí)慣，哪怕你不把它綁定你的社交媒體也是一樣。我已經(jīng)用Zite幾個(gè)星期了，而且我發(fā)現(xiàn)，我“頂”或“踩”的報(bào)道越多，它向我的個(gè)人頁(yè)面推薦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.