Month: March 2016

You Aren’t Who You Hang Out With

Every new app you try these days wants to know who your friends are. It’s easy to understand why. On the marketing side, it’s to encourage users to evangelize the app amongst their friends. On the user experience side, however, it’s to help users consume more relevant content.

Here’s are a few examples:

  • Upon signing up for Rdio and connecting your Facebook account, you are shown music your friends are listening to.
  • Upon installing Oink and connecting your Twitter account, you are shown food and other items your friends have sampled.
  • Upon checking your Facebook news feed, you are shown status updates from friends reacting to movies they’ve just seen.

While this sort of content tailoring provides value, I often find myself uninterested in it. The reason is that although in many cases my friends are similar to me, my taste in things like music, movies, and food do not map to my friends’. The taste correlation between friends may be greater than between two random strangers, but it’s still not very high in most cases.

There’s a better way to expose people to new experiences and I think we’ll start to see more of it in the future. It may already have a name, but I’ll call it “phantom friending”.

To illustrate phantom friending, imagine you want to watch a movie tonight and you need a recommendation. Now imagine you have these two options:

  1. Calling your best friend, asking them what good movies they’ve seen recently, and picking one of them.
  2. Consulting a list of preferred, recent movies put together by someone across the country who you don’t know but who has in the past indicated that they hate a lot of the same movies you hate and love a lot of the same movies you love.

I hold that in almost every case, the second option will provide a better result. Even if you were able to poll 5, 10, or 20 friends, a well-picked phantom friend would produce a better result. That is because the phantom friend doesn’t represent someone you like to socialize with — as your real friends do — but rather someone who watches movies the same way you do. They have your same tolerance for violence, same appreciation for special effects, and same patience for heavy dialogue. In other words, they may be unlike you in every other way, but their brain consumes movies the same way yours does.

The phantom friend concept works better for some subjects than others. It would seem to work well for movies, food, and music. It may work less well for TV shows, because a big part of TV shows is discussing them week after week with our friends. The same goes for clothing. We often wear similar clothing as our friends in order to fit in better.

For the many situations where phantom friends are better influencers on us, I’d love to see more apps and services geared towards this type of discovery. One example I’ve always wanted is a “Movie Critic Dating Game”. I rarely read movie reviews because I haven’t identified a movie critic who is a lot like me. Here’s how it would work:

  1. I am presented with a list of 20 movies.
  2. I rate each movie with a thumbs up, thumbs sideways, or thumbs down.
  3. The app finds me the national movie critic who has rated the 20 films most similarly to how I have rated them.
  4. I then begin reading the critic’s reviews each week and choose new movies to watch accordingly.

Interestingly, the above scenario works almost as well if the system can find someone with the exact opposite tastes as me. If I can find the person who I disagree with the most, I can just always do the opposite of what they suggest (the “Costanza strategy”). Furthermore, even if you extended the questionnaire to 200 movies, there is someone in the world (although perhaps not a professional movie critic) who answered all 200 the same way you did.

Undoubtedly I am not the first to think of this concept, but given that it doesn’t seem computationally ferocious to do, I’m surprised we haven’t seen more of it. Hunch seemed like it was after a similar result, but it always seemed too impersonal to me. I don’t want a computer telling me what people similar to me like. I want a computer matching me up with someone and then letting me know what else they like. There is a difference there.

I can imagine a world in which I have a movie sensei, a restaurant sensei, a music sensei, and a bunch of other senseis. I may eventually know them by name or I may not, but it would be a fun set of relationships to have.

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