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I was looking through the list of proposed panels for the next SXSW to see what talks and panels I could find about music recommendation. I found a number of interesting talks that were related to music recommendation, but none that seemed to answer the question about why music recommendation seems so broken and what is going to be done to fix it. Since I know a bit about the topic, I decided that perhaps I should propose such a talk. But I found out that I'm way too late ... the SXSW proposal deadline was July 11. However, the SXSW organizer that I contacted said that there is still a very small chance that if I submitted something that it could be accepted. He indicated that although the voting on submissions is closed, that if my proposal received some favorable comments, that would help. So although it is a long shot, I decided it wouldn't hurt to submit something, so here's my proposal:

I'm so sad, my iPod thinks I'm Emo. - Music recommendation is broken - automatic music recommenders make mistakes that no human would ever make. In this talk, we will explore why recommenders make such dumb mistakes and we will explore some of the new ideas coming from recommendation and music researchers to help make music recommendations better.

If you think it is interesting and may be something you'd attend at SXSW, I'd be pleased if you'd add a comment to the proposal.

Here is a list of other SXSW panel proposals that are somewhat related to either music discovery or recommendation. It looks to me like the most closely related panel is Music 2.0 = Music Discovery Chaos?. This one does look interesting and I'd love to attend it, although I don't think it is going to be too research-oriented.

• I'm so sad, my iPod thinks I'm Emo. - Music recommendation is broken - automatic music recommenders make mistakes that no human would ever make. In this talk, we will explore why recommenders make such dumb mistakes and we will explore some of the new ideas coming from recommendation and music researchers to help make music recommendations better.
• Keeping it Human in the Age of Big Data"The selection and placement of stories on this page were determined automatically by a computer program." - http://news.google.com Or was it? At Last.fm we've grappled with how to present and filter our user-generated music data, but also how to mix in true editorial content. Humanizing core features was much of the impetus for the recent re-launch.
• Music 2.0 = Music Discovery Chaos?The way we discover music has entirely changed in less than 10 years. Radio’s aging demo is presented with safe mainstream offerings. Music discovery is at the forefront of technology and social networks, yet no new standard has successfully been adopted. Websites abound attempt at both data and user generated rating/filter systems. Human VS algorithm: what method can save us?
• Neither Fish Nor Flesh: Music Discovery Going Semantic - Current music discovery techniques rely on human annotation and human behaviour to generate recommendations. We review current recommendation methods and take a in-depth look at new semantic recommendation methods, and discuss how both worlds can be combined to deliver an ultimately better music discovery experience.
• “Just For You”… Really? - Personalization technologies are all over the web now, with applications everywhere offering recommendations for movies, music, video, blogs and most anything else we might like online. Do any of these systems work, and if so how? Is “Discovery” the next “Search,” or just an excuse for behavioral targeting systems that compromise privacy? Finally… If you’re a site publisher interested in adding personalized recommendations, how do you pick a partner to help you get there, or strike out on your own and get the feature built????
• Collaborative Filters: The Evolution of Recommendation Engines - Recommendation engines have boomed in the era of social media. These panelists are experts at collaborative filtering systems. Citing Digg, Amazon, and Netflix as examples, they will have a high level discussion about the evolution of recommendation engines and how each approach is different.
• People Who Purchased This Also Purchased… What?? - Ever wonder how sites determine what music and movies to recommend? Ever received an off-beat recommendation that made you go hmm….? That’s because most sites use collaborative filtering – which determines recommendations based on what other users like. This panel will discuss how these technologies are evolving and the future of recommendations.
• Algorithms, Meta-Data, and Why the Future is Behavioral - Data about data or data with data? Social smarts versus machine smarts? Recommendation engines are finally delivering on helping us filter infinite choices while user generated ratings and reviews can now be found on almost any product or service. Where are these being combined to help users and what's next?