Between October, 2006 and March 2007 much of my free time was spent pursuing the Netflix Prize. Netflix was offering $1 million to anyone who could take the dataset they provided (containing 100 million ratings from 500 thousand customers covering 20 thousand movies) and make predictions for 2 million unknown ratings (from those same customers and movies) that were 10% better than Netflix's own predictions as measured by the root mean squared error (RMSE). The prize was eventually won.

Working on this challenge was fun and frustrating and a great learning experience. I worked on my early ideas in LISP before switching to C++ due to my inability to optimize LISP for performance. I exchanged the pain of slow execution for the pain of slow development and having to compile and reload all of the data into memory for each new test. I may eventually research LISP optimization (maybe using type declarations for all variables?) and write a language performance comparison page. I bet that would be a hit on Programming.Reddit.

If you came here hoping to read about my ideas or techniques I'm afraid my best scores come from using the ideas already discussed by others. I don't want you to go away empty handed so I compiled a list below of the web pages that helped me get my score.

• Simon Funk: Try This at Home
• Timely Development: Netflix Prize Results and Source Code
• Billy McCafferty: Using the Pearson Correlation Coefficient
• David Vogel / Ognian Asparouhov: Netflix Solution (link is no longer valid)
• Anil Thomas: Netflix Contest