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Music discovery services chart new paths for gift recommendations

Pandora, Last.fm, and iLike have broadened the appeal of recommendation algorithms and social discovery and sharing. Despite a challenging – even hostile - business environment, passionate entrepreneurs and music lovers are bringing to market new ways to explore and discover new music.

Mufin has gathered the most attention so far – being a spin off of the Fraunhofer Institut. Mufin relies exclusively on content analysis to make music recommendations. The service analyzes 40 characteristics for each the 4 million songs in its database, including tempo, sound density, and variety of other factors to filter out songs which are similar. This content-based approach uniquely addresses cold start and long tail discovery issues, but feels a bit too “literal” in the way it analyzes music similarities between songs.

The perceptron is an experimental and transparent collaborative filtering service. The service aggregates and filters recommendations made by actual humans (from tinymixtapes.com and epitonic.com), social links between artists and fans (e.g. belonging to the same label or the number of friends on a myspace page), and other social music sharing sources. Interestingly, recommendations sources and the relative weights of the various sources are all public. The service does a great job at mixing familiar and unknown recommendations, thereby making the new stuff all the more interesting. It would be terrific to see a next generation of this service allow users to actually tinker with sources and weights to obtain more personalized recommendations.

The next big sound pushes the music discovery envelope. The service does a great job at crowd-sourcing and uncovering new musical talents, using the music label paradigm. Each listener is allowed to select and sign up to 10 artists at any given time among all the unsigned artists that broadcast their music on the site. This site is a great place for unadulterated creative talent, although a bit of collaborative filtering would enhance the listening experience.

With Black Friday upon us, we are now entering the Holiday season. Many publications have started churning out their gift recommendation lists – from the top vintage geek gift list to the New York Times’ favorite 100 books for the year to Amazon’s top reviewers’ recommendations for the Holidays.

Wouldn’t it be great to have a service that compiles a personalized gift list, based on deep content analysis between products, expert recommendations, social insight and an Etsy-like indie product-sourcing twist to add a few one-of-a-kind gifts?

There are a few early signs of this future yet to come. Semantic Gifts - currently in alpha - mines your friends’ profiles on twitter, facebook, friendfeed, and their personal blog. Giftag and Amazon’s Universal Wish List now provide a mine of user-generated gift lists. BlackFridayAds and BlackFriday now aggregate black friday coupons and special deals accross thousands of online and brick-and-mortar retailers. If you still can’t find the perfect gift, there’s always Paul McCartney’s self-produced album – Electric Arguments.