Matchmaking north vs the rest 2014


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The patent part, though, would be the passage. Jakarta plumber club.


OkCupid lets users see the responses of others, but only to questions they've answered themselves. McKinlay watched with satisfaction as his bots purred along. Then, after about a thousand profiles were collected, he hit his first roadblock. OkCupid has a system in place to prevent exactly this kind of data harvesting: It can spot rapid-fire use easily. One by one, his bots started getting banned. He would have to train them to act human.

He optimized in a first mover from home and increasing it into the accounting department's broadband line so it could run bidirectional 24 hours a day. England dating tribal.

He turned to his friend Sam Torrisi, a neuroscientist who'd recently taught McKinlay music theory in exchange for advanced math lessons. Torrisi was also on OkCupid, and he agreed to install spyware on his computer to monitor his use of the site. With the data in hand, McKinlay programmed his bots to simulate Torrisi's click-rates and typing speed. He brought in a second computer from home and plugged it into the math department's broadband line so it could run uninterrupted 24 hours a day. After three weeks he'd harvested 6 million questions and answers from 20, women all over the country. McKinlay's dissertation was relegated to a side project as he dove into the data.

He was already sleeping in his cubicle most nights. Now he gave up his apartment entirely and moved into the dingy beige cell, laying a thin mattress across his desk when it was time to sleep. For McKinlay's plan to work, he'd have to find a pattern in the survey data—a way to roughly group the women according to their similarities. The breakthrough came when he coded up a modified Bell Labs algorithm called K-Modes. First used in to analyze diseased soybean crops, it takes categorical data and clumps it like the colored wax swimming in a Lava Lamp. With some fine-tuning he could adjust the viscosity of the results, thinning it into a slick or coagulating it into a single, solid glob.

He played with the dial and found a natural resting point where the 20, women clumped into seven statistically distinct clusters based on their questions and answers. Another pass through K-Modes cs that they clustered in Marchmaking similar way. His statistical sampling had worked. Now he just had to decide which cluster best suited him. He checked out some profiles from each. One cluster was too young, two were too old, another was too Christian. But he lingered over a ths dominated by women in their mid-twenties who looked like indie types, musicians and artists. This was the golden cluster.

The haystack in which he'd find his needle. Somewhere within, he'd find true love. Actually, a neighboring cluster looked pretty cool too—slightly older women who held professional creative jobs, like editors and designers. He decided to go for both. He'd set up two profiles and optimize one for the A group and one for the B group. He text-mined the two clusters to learn what interested them; teaching turned out to be a popular topic, so he wrote a bio that emphasized his work as a math professor. The important part, though, would be the survey. He picked out the questions that were most popular with both clusters.

He'd already decided he would fill out his answers honestly—he didn't want to build his future relationship on a foundation of computer-generated lies. But he'd let his computer figure out how much importance to assign each question, using a machine-learning algorithm called adaptive boosting to derive the best weightings. Which dating sites are linked.

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