While our recommendation system may not have taken the typical textbook approach, nor may it be the most accurate recommender possible, it does represent a fully demonstrable and incredibly interesting approach to one of the most commonplace techniques in data science today. Further, with persistent data storage, a REST API interface, distributed shared memory caching, and a modern web 2.0-based user interface, it provides a reasonably complete and rounded candidate solution.
Of course, building a production-grade product out of this prototype would still require much effort and expertise. There are still improvements to be sought in the area of signal processing. For example, one could improve the sound pressure and reduce the signal noise by using a loudness filter, http://languagelog.ldc.upenn.edu/myl/StevensJASA1955.pdf, by extracting pitches and melodies, or most importantly, by converting stereo to a mono signal.