Late last month, Twitter open sourced an algorithm that’s designed to ease the computational burden on systems trying to recommend content — contacts, articles, products, whatever — across seemingly endless sets of possibilities. Called DIMSUM, short for Dimension Independent Matrix Square using MapReduce (rolls off the tongue, no?), the algorithm trims the list of potential combinations to a reasonable number, so other recommendation algorithms can run in a reasonable amount of time.
Reza Zadeh, the former [company]Twitter[/company] data scientist and current Stanford consulting professor who helped create the algorithm, describes it in terms of the famous handshake problem. Two people in a room? One handshake; no problem. Ten people in a room? Forty-five handshakes; still doable. However, he explained, “The number of handshakes goes up quadratically … That makes the problem very difficult when x is a million.”
Twitter claims 271 million active users.
DIMSUM works primarily in two different areas: (1) matching promoted ads with the…
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