Game On: Playing Our Way to a Better Taste Graph

By Marc Ruxin , written on August 1, 2012

From The News Desk

Why isn’t the social Web better at helping us find things we like, and find other people whose tastes we share? Why can’t Facebook or Tumblr understand that you specifically like roots rock, or down-tempo electronica, or small-budget foreign films, or non-superhero blockbusters, and make recommendations accordingly?

That kind of discovery, informed by the interests and experiences of our peers instead of pundits, has long been a key promise of social. Yet here we are in 2012, still spending too much time searching for needles in haystacks and diamonds in the rough. The broad social networks consistently fail to identify and surface the nuances of personal taste.

Much of the problem comes down to the sheer volume of social behavior. The Web has empowered tastemakers everywhere to have a voice, but with billions speaking out at once, quality and specificity can be nearly impossible to discern. The next chapter of the social Web will need to include tools and services that help us find relevant content and like-minded people across the increasingly long tail of passion and culture.

This challenge has been addressed many times over the years, from many angles, with varying degrees of effectiveness -- Amazon’s collaborative filtering, Pandora’s music genome, Netflix recommendation algorithms, IMDB user ratings, Metacritic scores -- but a satisfying solution has remained out of reach.

The broad-based social platforms simply aren’t engineered to make it simple for us to share, discover, and build vertical personal brands around specific interests. Remember when you registered for Facebook and were asked to list your favorite bands and films? Did you even bother to do so? If you did, have you ever updated that information as you’ve made new discoveries and your tastes have evolved? As an engine of discovery, that profile is conspicuously underpowered.

Spotify helps you publish and track your current listening, but it doesn’t point you toward the next band you’re likely to fall for. Twitter may surface the occasional singer as a trending topic -- as likely as not, following a drug overdose or police encounter -- but unless you take the time to optimize TweetDeck and other aggregators around publishers in specific categories, you’re not going to get much help with your vertical tastes. Tumblr fields many vertically-minded publishers, but the vast number of blogs being authored and updated on a daily basis easily overwhelms attempts at discovery. Pinterest is fueled by our desire to share and associate ourselves with specific “interests,” but skews heavily to towards product- and ecommerce-based expression, not culture.

What would an effective mass taste graph look like? There are three essential elements: mass adoption (to ensure variety and depth); frequent user engagement (to reflect dynamic tastes); and the accurate representation of real-time trends.

As it happens, these criteria have already been achieved in another area of the social Web: fantasy sports. Tens of millions of people play an average of 20 minutes of fantasy sports every day in categories from cricket to baseball. They track the latest stats and information, anticipate the next big thing, and interact with fellow enthusiasts through competition, player trades, and -- of course -- endless opinionating and commentary. The conversations among members of a specific league are focused to the point of obsession, and provide the ultimate social network for hardcore sports enthusiasts.

That’s the kind of energy, engagement, and relevance that it takes to build a vibrant and dynamic taste graph. There’s no reason the same model that powers fantasy sports online -- in essence, gamification -- can’t be extended into verticals like music, television, film, even politics and food.

After all, building and maintaining a user profile or an entire social network should be fun, like a game -- not feel like homework. Properly architected game mechanics, cultivated competition, viral hooks, and meaningful data can draw like-minded people together to form thriving communities of interest. Personal influence and its measurement will also have a role to play in the taste graph. Klout measures broad non-vertical influence as it relates to a person’s social reach; we should also have a way to find or follow like-minded influencers within a specific interest.

The desire of consumers for social engagement around vertical interests can be seen in the rise of smaller services like Foodspotting for foodies and Instagram for shutterbugs -- though even these are somewhat broadly defined, and lack the gameplay aspect needed to build frequent, long-term engagement. To take the next step toward a truly useful taste graph, we need to design social experiences that are as compelling and addictive as the interests that fuel our passions.

For any of you looking for some new summer music dial into TastemakerX V.4 "Summer Songs".

[Image courtesy er00mb0b]