{"id":640935,"date":"2013-02-01T13:11:45","date_gmt":"2013-02-01T18:11:45","guid":{"rendered":"http:\/\/gigaom.com\/?p=606615"},"modified":"2013-02-01T13:11:45","modified_gmt":"2013-02-01T18:11:45","slug":"gravity-giving-away-personalization-to-whichever-publishers-want-it","status":"publish","type":"post","link":"https:\/\/mereja.media\/index\/640935","title":{"rendered":"Gravity giving away personalization to whichever publishers want it"},"content":{"rendered":"

Gravity<\/a>, 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. If you don\u2019t mind a few sponsored stories popping up in the newsfeed \u2014 a condition of using the free platform \u2014 this could be a pretty good deal.<\/p>\n

Gravity\u2019s recommendation system is based on its interest graph<\/a> technology, which we detailed last year. Here\u2019s how I described it then<\/a>:<\/p>\n

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[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\u2019re done, the humans step in one more time to kill any bad connections between terms.\u00a0The result is a system that can determine with high accuracy that a person tweeting about Vanessa Laine (Los Angeles Laker Kobe Bryant\u2019s ex-wife), for example, is probably more interested in basketball than about Laine\u2019s date of birth or other accurate but irrelevant information.<\/p>\n<\/blockquote>\n

As new content streams into Gravity\u2019s system, it\u2019s analyzed and categorized in real time, then presented to users accordingly based on their interests and behavioral history.<\/p>\n

\"How<\/a><\/p>\n

How Gravity\u2019s platform works<\/p>\n<\/div>\n

Graph processing and graph databases<\/a> \u2014 which store and analyze data based on their relationship to one another \u2014 are critical to our onlines lives, powering everything from online recommendations<\/a> to social search<\/a> to knowledge discovery<\/a>. Graph technologies are also the focal point of some impressive life sciences work from companies such as Syapse<\/a> and Ayasdi<\/a>, which will be presenting at Structure: Data<\/a> in New York next month.<\/p>\n

But publishers struggling to stand out on a noisy web might have the most to gain from graphs and personalization, generally. At our PaidContent Live<\/a> conference (April 17 in New York), executives from Prismatic, Zite and Bluefin Labs will take the stage to talk about the importance of personalization for helping consumers filter through the deluge of content online so they can find what they really want. It\u2019s arguable that the trick to keeping readers happy is knowing what they want to read \u2014 possibly better than they do themselves.<\/p>\n

According to Gravity, its platform currently \u201cdelivers more than 25 million personalized content recommendations per day to more than 200 million users. Beta partners have reported click through rates two to three times above previous levels, return visitation increases of 300 percent and session length increases up to 40 percent.\u201d<\/p>\n

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