connecting*the*dots

If you happen to be someone who thinks all this 2.0 hub-bub about social tagging and meta-data is confusing, I’ve found the perfect domain for us to reverse engineer together.

In order to get us on the same page, why don’t you first hop on over to Musicovery — an online radio site with an extremely interesting interface — and play around for a bit (be sure to explore all of the feature found on the controller).

Just don’t forget to come back! I promise that we’ll have some fun and you might even learn some geeky information architecture stuff.

Welcome back.

Okay, so how brilliant was that experience?

I don’t know about you, but discovering music based on my current mood fills a huge void in how I currently listen to music. Before discovering Musicovery, the closest I could come to replicating such a dynamic experience in iTunes was by creating a playlist for a specific genre and shuffling the playback.

And that just doesn’t do it for me. (more on the genesis of genres later)

Essentially, everything that Musicovery is doing is made possible by leveraging the relationships between meta-data applied to discrete information objects. So, are you up for digging further into the underpinnings of this puppy to figure out how it works and possibly come up with a few meta-data driven enhancements to the current user experience?

I’ll take your silence as a yes. Alright, let’s get to it then.

Old School, Structured Meta-Data

Deconstructing music (as an information object) is pretty straight-forward, as each song comes with standardized attributes that neatly fit into industry-wide delivery and marketing mechanisms (which were established well prior to the explosion of the dynamic nature of the web).

Okay, first, let’s list the most commonly exposed and explicit attributes of a song. My top six would be:

• Artist name
• Song Name
• Album name
• Release Date
• Track Length
• Genre

Now, while the first five attributes are all explicitly defined — the artist’s name is the artist’s name, etc. — the sixth attribute (genre) is only explicit when viewed through the lens of the music industry’s nomenclature levers (a song that I consider to be hip-hop, someone else might call rap, while the music industry itself might label it as pop).

By managing the evolution and edification of genre nomenclature, the music industry uses these silos to market acts with a much greater degree of certainty in matching the expectations of the customer because the music industry is creating those very expectations themselves through this process.

Deep, huh?

So back to deconstruction; let’s see how Musicovery is leveraging these primary attributes (if at all):

• Each song displays the artists name
• Album name isn’t exposed
• The controller interface allows the user to narrow results by decade or specific year based on the release date
• Track length isn’t exposed
• Genre is displayed prominently in the controller as the primary filter of returned songs

Two of the six most prominent song attributes aren’t being used, yet there’s a preponderance of controller functionality left to discuss.

Something else is going on.

Meta-Data In The Digital World

The aforementioned attributes of the song object have been around forever; they are the core identifiers for a song and always will be. As I mentioned before, the music industry has become extremely efficient in managing the relationships between these attributes across an expanding universe of songs — it’s their lifeblood. This particular set of meta-data fit the strategy of the analog age of information — where meta-data was constrained to the physical dimensions of the record’s liner notes or the pages of an industry magazine.

Now, in the Information Age, there are truly no limits to the amount or types of meta-data that can be generated; the only limitation — from a practical, business perspective — would be in how these new attributes fit into the domain’s value equation.

So, because the folks behind Musicovery have focused on creating a radio application that exposes music in particular ways (other than shuffled programming or human dj’ing), it’s a solid bet that they’ve expanded upon their meta-data set.

The Nitty-Gritty Attribute Model

In order to return a song by clicking on a specific spot in the mood or dance interfaces, the quadrants need to be explicitly defined to hook up with corresponding attributes applied to songs in the Musicovery universe. So what type of attributes would we need to add to each song? Here’s one approach:

Mood Interface

• Dark to Positive attribute scale (-5,-4,-3,-2,-1, 0, 1, 2, 3, 4, 5)
• Calm to Energetic attribute scale (-5,-4,-3,-2,-1, 0, 1, 2, 3, 4, 5)

Dance Interface

• Dance (-) to Dance (+) attribute scale (-5,-4,-3,-2,-1, 0, 1, 2, 3, 4, 5)
• Tempo (-) to Tempo (+) attribute scale (-5-,4,-3,-2,-1, 0, 1, 2, 3, 4, 5)

The range could be much more refined than 11 data points — theoretically, it could be as refined as equating to the number of pixels that reside in the actual interface — but due to the current size of the song universe (it seems limited, as I get repeat results somewhat often) and the already subjective nature of assigning such attributes to songs, this degree of differentiation would probably suffice.

Now, let’s take the mood interface and chop it up along these lines to visualize how each song could be found in this manner:

That’s pretty much it.

So while there are numerous choices one could make in the presentation (depending on the size of the song universe, the visualization would span out to neighboring squares to present a full return, etc.), in order for a song to be accessible by any aspect of the Musicovery interface, each song object would simply need to have the following structured data applied to it:

• Artist name
• Song Name
• Release Date
• Genre
• Dark to Positive attribute scale (-5,-4,-3,-2,-1, 0, 1, 2, 3, 4, 5)
• Calm to Energetic attribute scale (-5,-4,-3,-2,-1, 0, 1, 2, 3, 4, 5)
• Dance (-) to Dance (+) attribute scale (-5,-4,-3,-2,-1, 0, 1, 2, 3, 4, 5)
• Tempo (-) to Tempo (+) attribute scale (-5-,4,-3,-2,-1, 0, 1, 2, 3, 4, 5)
• A Billboard ranking (0,1) in order to display whether the song was a hit or not

Most of these data points could be data entry for a trained monkey, but the scaled meta-data is such a subjective determination that the resulting experience will vary from person to person.

Aside from scouring for top, authoritative talent like Kennedy (eh, for early 90’s music) and pay her thousands upon thousands of dollars to “moodize” and “dancize” each song and then splash her grill on the interface to pimp the brand, what else could we do to improve the resulting experience?

If you know me at all, you know where I’m going with this.

Why have only one person or team from one domain attributing mood or dance settings to all music, when the openness of the web has already proven models for empowering each user with the ability to add their own meta-data to the mix if they should chose to do so?

Open Up The Gates

Way back in the day, Launch.com (now Yahoo! Music) was the king of the internet radio scene. And while I dug being able to subscribe to other user’s services through their social network, my favorite feature, by far, was the ability to rate my music on a 0 (never play again) to 100 scale, in increments of 1.

Sure, maybe 101 levels was over the top, but future playback of my favorite music was amazingly accurate. Now, what if Musicovery allowed this same type of two-way interaction?

Here’s an example scenario:

I just clicked on the mood interface between the energetic and dark nomenclature. The first song that returned was Joe Cocker, With A Little Help From My Friends.

Really? Dark and energetic? I don’t think so. But as it is, I can’t affect the centralized intelligence of Musicovery. I just have to take their recommendations at face value.

Now, what if we were to add user input into the song interface?

Once we added our perspective on mood, the system could return the results to the information object and use the input in two ways.

• The meta-data could be lumped into all user feedback to present a more representative mood interface — the wisdom of the crowd if you will
• It could also be used to present personal mood results, from a toggle setting in the interface

If the song universe was large enough, we could add a similar rating control that Launch employed, so not only would our mood expectations be met, we’d hear our favorite songs more often as well.

Fun stuff.

Tags: design, experience design, information architecture, interface, internet, iTunes, metadata, music, Musicovery, tagging, Yahoo!.