“Do you want to get dinner next week?” In my experience, this question inevitably leads to a long quasi-negotiation, conducted via email or text message. It usually goes something like this:
Friend 1: Sure! Do you have a place in mind?
Friend 2: Not really! I’m up for anything. Maybe we could meet in Brooklyn after work?
(At this point we’ve narrowed it down to a borough of 2.6 million people. Doing great!)
Friend 1: Sounds good. Are you in the mood for going someplace nice or something more low-key?
Friend 2: Hmm, I don’t know. Maybe we could go with pizza to split the difference?
Friend 1: Oooh, let’s do pizza. There’s Franny’s, Saraghina, Motorino, Roberta’s …
Friend 2: Well, Roberta’s has that outdoor bar area you can wait in. If the weather’s nice, we can go there, and otherwise, we can do Franny’s?
Because of two people’s desire not to be perceived as pushy and/or their general indecisiveness, a decision that could have been made in two steps ends up taking six. And if there’s a Friend 3 involved, expect to add at least another couple of steps—unless Friend 3 is vegan or deathly allergic to gluten, which usually helps narrow down the choices. (By the way, the above dialogue is based on an actual recent discussion. We went to Roberta’s.)
Deciding where to eat, drink, relax, and chat with friends should be a pleasure, but instead it’s an engine of hesitancy and chagrin. As a result of that hesitancy and chagrin, you often end up going to the same handful of tried and true restaurants instead of branching out. What if technology could solve this problem by collecting a party’s various dietary, monetary, and atmospheric preferences and producing a restaurant that will delight everyone?
Ness was an app that promised to do just that. (I say “was” because it was acquired by OpenTable in February and subsequently shut down operations in order to incorporate its algorithm into OpenTable’s framework. More on that later!) Ness looked like the Netflix of restaurants: It invited you to rate restaurants you’d been to, then suggested other restaurants based on ratings made by people with similar preferences to yours. Taking a cue from OkCupid, Ness expressed its prediction of how much you’d like a restaurant as a “like percentage”—the higher the percentage, the stronger the recommendation. Ness’s “Recommendations With Friends” feature also figured out where your preferences and those of your friends overlapped.
I decided to put Ness’ algorithm to the test by recruiting colleagues to try a restaurant for the first time using the app. I made them promise that they would go to whatever place Ness recommended for us, and 14 brave co-workers signed up.
Which is where I ran into my first problem: Ness’ group recommendation algorithm accommodated 10 people maximum. I split us randomly into two groups and then immediately hit my second problem: Ness could recommend a restaurant for each group, but it couldn’t help us figure out what night we were all available to dine. We ended up using Doodle to find a night that worked for every member of each group.
Now for the moment of truth: I plugged in the names of the eight people in my group, chose “dinner” as our meal, and limited the geographical area to the West Village, near Slate’s office. (I also limited our price range to $ or $$—Ness’ ratings go up to four dollar signs. We work in journalism, not finance.)
With all this information, I expected Ness to spit out the name of the single restaurant with the highest average “like percentage” for all eight of us. Instead, it gave me a list of restaurants; confusingly, the list changed every time I refreshed the page. The main thing that had attracted me to Ness—that it would eliminate all decision-making—turned out to be a vicious lie. I still had to make a decision!
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