Break Anonymized Bill Totals into Individual Item Costs


Michael Fleder, an MIT researcher and recent alumnus working with the Laboratory for Information and Decision Systems (LIDS), had been working on an algorithm that could break down anonymized bill totals into individual item costs, creating an overview of how many people are buying a specific item or service. He was testing it out on a bulk set of data from Netflix, and although most of the data points matched to a list of the usual subscription services, there was an outlier that kept popping up at a price point too high for anything Netflix was offering.

On closer examination, Fleder realized that the algorithm was working better than expected — not only had it found known services, but it had also discovered an unannounced-but-rumored Ultra HD subscription that Netflix was testing on a limited audience. It also discovered another as-yet unmentioned product at an even higher price point.

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Dr. Michael Fleder