Publications

Intelligent Enterprise, Seth Grimes, January 3, 2008

The $1,000,000 Netflix Prize competition has produced interesting results, even if no winner, 15 months in. Some of those results are a bit surprising; others we should have expected but didn't anticipate. So while participants haven't yet bettered the accuracy of Netflix' Cinematch recommendation algorithm by 10%, the threshold to win the $1 million prize, we can still take away lessons about predictive-analytics fundamentals.

I recently checked on competition status after receiving a note from Alex Lupu, VP Marketing USA for Scio Systems; Alex has been keeping me apprised of his company's progress toward launch of property-lease abstracting and analyzing tools. Like Alex I'm into text analytics, and I liked his take that "intelligent communication between customer and the [Netflix suggestion] system" could provide an alternative route to better recommendations. Alex sees analysis of "'open questions' that allow customer to write a sentence or two" about movies as potentially beneficial in complementing traditional, pure-numbers predictive modeling. Alex says "assuming the customer is a static entity seems wrong to me, thus looking at databases only is not of much help."

Coming from another angle, the thought that you can fit a training set without truly worthwhile real-world implications, knowledge-discovery guru Gregory Piatetsky-Shapiro seemed to agree: "Since the contest is based on a fixed data set, it is theoretically possible to find the optimal solution for it after a few million tries (:-). However, after the progress reached about 7% it slowed down significantly."

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