Program Directors > Recommenders

The endless debate on the best approach for providing music discovery experiences continues rage. This continued with a SxSW panel put on by Paul Lamere (from The Echo Nest) and Anthony Volodkin (from The Hype Machine). For some background, here is their presentation:

Help! My iPod thinks I'm emo.

View more presentations from Paul Lamere.

Additional notes from Anthony are here: http://fascinated.fm/post/89782283
And notes from Paul can be found here: http://musicmachinery.com/2009/03/26/help-my-ipod-thinks-im-emo-part-1/

My take? There are many ways to classify these different approaches, and some can be considered subsets of others. But, in short there are more approaches than most can shake a stick at... with some of the more notable examples being (as I see it):

• "Musicologist"/Expert Analysis - a hundred people with headphones on categorize songs on a few hundred different attributes (e.g. minor chords, upbeat, heavy piano)
• Content-Based/Waveform Analysis - a computer looks at the sonic attributes of a song (e.g. tempo, harmonic range, etc) and then looks for other songs that posses similar attributes
• Collaborative Filtering - people who listen to/buy/highly rate song X, also have a high correlation to buying/listening to/highly rating song Y
• Editorial (e.g Bloggers, Pitchfork, Radio) - some one broadcasts their opinion... "hey, these guys sound like so and so and they are good. I give them 5 stars".
• Semantic (e.g. tag clouds) - by matching tags, genres and sentiment keywords (either manually input by users or extracted from web articles)
  
• Curator (e.g. playlist sharing) - community generated "top song" and "just listened" charts, curated playlists, TV/movie soundtracks. These could be actively curated lists of songs (e.g. playlists) or passively programmed by the curator (e.g. "just listened").
• Shuffle - just a random selection of tracks from a fixed set of songs
• Biographical - this artist you like was influence by this other artist or was also in this other band
  
• Friend-to-Friend - the most common, one friend tells another... "hey, have you heard X? You'd like them".

For each, you generally have two sources of information which to base your recommendations on:

• Implicit - input/feedback is collected passively/automatically (e.g. listening history)
  
• Explicit - input/feedback is solicited and manually provided (e.g. user ratings)
  

Some of the approaches above are exclusive to one type of data input (e.g. "editorial" is explicit feedback only), but most incorporate both.

And, as we speak there are hundreds/thousands of Music Information Retrieval PhD candidates figuring out new approaches, there are new spins (and terminology) on the existing methodologies, and companies combining two or more of the approaches above. Having spent some time in the discovery/recommender space myself, my take is this...

There is no magic bullet.

We all discover content everywhere, and from every context.... friends, TV soundtracks, recommendation engines, radio, etc. In my opinion there is no such thing as a "better recommendation".... there is only a "good enough" (in that what I discovered was satisfying) or a "bad" recommendation. What is required in both cases is transparency as to why it was recommended. I say often when it comes to recommendations... "the why is usually more interesting than the what". That is because the "why" helps user identify the sources that they trust, can relate to, and can be turned to again in the future for inspiration. Sometimes that source may be a machine, sometimes it may be an editor, sometimes it may be a chart. They all have their value, and they all serve the same purpose.. to be one my personal "program directors".

I had a conversation this morning with J T. Ramsay where he told me: "[music] discovery is one of the biggest fallacies of all time. If people were so amped on discovery, radio would have been formatted differently." And I have to admit, I don't disagree.

What consumers have continually asked for is "programming", whether is be playlist builders, charts, "just played" data, recommendation engines, DJs, mixtapes or CDs. Basically, it just comes down to "don't force me to make a decision every 4 minutes about what I'm going to hear next". As music services continue to move to small UI devices where search and destroy is not a viable use case (mobile, car stereos, etc.), programming becomes even more essential.

The title of this post "Program Director > Recommenders" is not meant to imply that they are different and that one is better.... what I mean is that one is a superset of the other. Trying to pit them in a battle with each other is akin to asking... "which is better when you are hungry, a sandwich, a hot dog, or an apple?". The answer for me is, "they will all do the trick... it's just a matter of what temporal mood I'm in weighed against how much effort I have to expend to get each ."

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