I’ve recently been mulling over the question, “Why does Genius suck so much?” and the implications that it has.

Genius is the playlist generation tool in Apple’s iTunes music software. You choose a song that you’re in the mood for, and it creates an entire playlist of similar songs. Essentially, its a recommender system; if you like x you’ll like y. The problem is that you get a very narrow point of view, with very little genre skipping. and no pleasantly clever surprises.

What sets Genius apart from other song recommender systems is that its essentially powered by the crowds. Apple has the luxury of a rich data set of habits and rating, and it appears to factor heavily into the recommendations. Indeed, algorithmic playlist generators were creating better results years before Genius came on the scene. So, what does this mean for the crowd?

The fact that computers can be better than humans in understanding art is off-putting. I’m still working through this problem, but here are some thoughts toward untangling it.

Ratings data is emotionless. When you rate a song 1 or 5, you’re giving it a universal ‘like’/’dislike’. This data doesn’t factor the mood of the song or the emotion of the listener. This is all very removed for circumstance. As I suggested to Bill Turkel, perhaps such simple crowd-based recommendations are better for high-level suggestions, like artists you may like, but useless at the micro-level (unless that data crowds are contributing is more specific to the topic of recommendations). In contrast, technology can quite effective interpreting the types and patterns of sound which represent an emotion. Certainly it can’t easily understand whether a song is good, but if you want a slow, jazzy rock song, that’s fairly achievable. This is something in which music recommendation is fairly unique, as it is easy to interpret than it would be to interpret thousands of movie plots or millions of book themes.

Despite this, perhaps the most-cited example of a good music recommender is Pandora, which is an internet radio based on the Music Genome Project (MGP). The MGP does use humans to categorize songs, having professionals tag each song with over 400 tags and using an algorithm to weigh the values. Pandora’s success shows that humans are indeed effective at understanding music, given that they’re looking at it in the right way.

There’s also the effect of popular media that makes human-based recommendations unbalanced. If a lot of people like Coldplay, the range of music that it will be recommended for will be broad. This additionally creates an echo loop where popular music simply grows in popularity. Inversely, it is very difficult for new music to enter the loop. If everybody that likes The Strokes like Yeah Yeah Yeahs, the recommender will reinforce this, brushing aside any similar new bands.

However, such problems are limited to the balance of the algorithm. Last.fm, which tracks all of its users’ listened music, is fairly effective in recommending similar music. Also, because of their detailed information on what a user has listened to, they can suggest less listened to songs. Though they don’t offer playlist generation, I wouldn’t put this beyond their abilities.

So where do crowds factor in here? If anything, Pandora suggests that this is best left to professionals. Certainly, you can’t get that sort of exhaustivity with crowds. The answer may lie in reliability. Large groups would be able to make much simpler connections, but on a larger and more verified scale. When I make a playlist with Lou Reed’s Take a Walk on the Wild Side, I always follow it with Urge Overkill’s Girl, You’ll Be A Women Soon.  The songs are linked very little, but there’s something in me that recognizes the similarly cool feeling that I feel. If you could somehow capture millions of these sorts of links, that could lead somewhere.


In early June 2007, I shared the following twitter:

Idea: ‘YouShould’, a suggestion site where people write open letter suggestions of ideas for companies, authors, and services

There had been two things on my mind. The first was the potential benefit to consumers that such feedback could allow.  I was inspired by Gmail’s suggestion page, where one can suggest what they would like to see implemented in Gmail next. Google appears to take it seriously, too, listing past suggestions that have already been implemented. The other reason for my idea was that I had been brainstorming for my senior thesis, which was beginning in September. However, once September rolled around, “YouShould” was crushed by the release of the similarly named Should Do This.  While perhaps no exactly what I had imagined, it was pretty darn close.  I don’t believe in reinventing the wheel, so I dropped the project.

Turns out, dropping the project was probably a good idea.  After Should Do This, there came IdeaScale, and CrowdSound, and Suggestion Box, and UserVoice, FeatureList, Fevote and CollabAndRate.com.  All of these had different approach to the same concept: getting feedback from customers.  Turns out I wasn’t alone in the concept.

Unfortunately, as tends to be the rule, none of these services seem to have gained any traction. Interesting on paper, there was not enough return to attract critical mass and make the idea suceed. One reason is that, with unsolicited advice, users do not gain a sense of contribution. One thinks, what are the odds that a company cared enough to seek out these websites?  Users want to offers their thought and suggestions, but they also want to be heard. It’s like that wonderful game my aunt always played with the kids: “who can stay quiet for the longest”. Sneaky, yes, but we certainly stayed quiet for longer than we would have simply for its own sake. This is why general suggestion boards have been failing, and crowd-suggestion businesses has been moving into infrastructure, offering tools that enable business to ask their customers themselves.

How many times have you liked a television show, and found yourself lamented the fact that —unless you’re directly being asked by Nielson or BBM— your patronage does not actually register? The broadcast system that television uses is by definition clunky: it transmits only one way, from one to many, without a direct capacity for information feedback. This simple concept was outlined in the Shannon-Weaver model of communication back in the 40s. However, while the flow of source > encoder > message > channel > decoder > receiver is adequate for describing technology, attempts to apply it to human communication have been notably shortchanged. It simply is not natural to our nature, not reflective of how humans negotiate meaning. The transmission model is not simply limited to delivery of television and radio signals. In a way, our entire consumer culture attempts this few-to-many transmission. Business online, however, exists within a system constructed to be (though not always realized as) many-to-many. Feedback is the nature of the internet. If you’d like to see organic cotton shirts at the Gap, the time investment in doing so would discourage casual contributions. More likely, your feedback would be much more crude, by shopping elsewhere, in which case the Gap is left trying to figure out why you did so. In contrast, a Gmail user conscious of the idea solicitation page can quickly send in a thought when they have it.

“It’s not the cost we’re looking at, it’s how we are making the application better for the consumer” —Jari Pasanen, Nokia VP for innovation acceleration (BusinessWeek.com)

In “How Nokia Users Drive Innovation“, Business Week outlines Nokia’s solicitation of its users for ideas, and the sucess that that have been having. Other companies that do so are Starbucks (My Starbucks Idea), Salesforce (SalesForce IdeaExchange), and Dell (Dell IdeaStorm). In these example, communities have formed around supporting and expanding on ideas. A cynical observer would suggest that these companies are looking for free business advice. The reality, however, is that it is in the best interest of customers to help build better products for themselves. Companies are constantly looking for feedback and those that respond as the people for whom the company adapts to. This idea is nothing new; what has emerged is the persistance and tenacity of users in doing so when given the proper tools.