Gravity giving away personalization to whichever publishers want it
Gravity, a Santa Monica, Calif-based startup that personalizes reader content for web publishers, is opening up its recommendation engine to anyone that wants to use it.
[T]he gist is that humans first serve as guides for machine-learning algorithms by determining connections between terms within large data sets, then the algorithms take over to complete the job faster than humans ever could. When they’re done, the humans step in one more time to kill any bad connections between terms. The result is a system that can determine with high accuracy that a person tweeting about Vanessa Laine (Los Angeles Laker Kobe Bryant’s ex-wife), for example, is probably more interested in basketball than about Laine’s date of birth or other accurate but irrelevant information.
Graph processing and graph databases — which store and analyze data based on their relationship to one another — are critical to our onlines lives, powering everything from online recommendations to social search toknowledge discovery. Graph technologies are also the focal point of some impressive life sciences work from companies such as Syapse and Ayasdi, which will be presenting at Structure: Data in New York next month.
But publishers struggling to stand out on a noisy web might have the most to gain from graphs and personalization, generally.