Dating is a search problem solve it with google

Rated 3.81/5 based on 626 customer reviews

His wife is also Indian, and they were introduced through family.

Yet Thombre says his experience at i2, where he spent years finding ways to move products around the country more efficiently, was perfect preparation for the online dating industry.

People were doing something very different from the things they said they wanted on their profile."As a result, Match began "weighting" variables differently, according to how users behaved.

For example, if conservative users were actually looking at profiles of liberals, the algorithm would learn from that and recommend more liberal users to them.

Thombre had attended the prestigious Indian Institute of Technology Bombay, then taken an advanced degree in chemical engineering at the University of Arizona.

Like his boss, he met the love of his life offline.

Indeed, says Thombre, "the politics one is quite interesting.

dating is a search problem solve it with google-47

dating is a search problem solve it with google-16

Despite being in a mid-rise office tower overlooking a turnpike in the dry, landlocked city of Dallas, Texas, the Match offices are evocative of a racier environment, where anything might happen.

So, if a woman says she doesn't want to date anyone older than 26, but often looks at ­profiles of thirty-somethings, Match will know she is in fact open to meeting older men. That is, the algorithm looks at the behaviour of similar users and factors in that ­information, too.

Until Ginsberg joined IAC, which owns Match, in 2006, she worked at i2 Technologies, a supply-chain management company, also based in Dallas.

"When you give it stimuli, it forms neural pathways," he says. It's learning as you go." The same principles are powering the recommendation engines at popular sites around the web.

Amazon uses similar ­technology to recommend new products for people to buy, Pandora learns from likes and dislikes to customise its internet radio stations, and Netflix famously offered

Despite being in a mid-rise office tower overlooking a turnpike in the dry, landlocked city of Dallas, Texas, the Match offices are evocative of a racier environment, where anything might happen.

So, if a woman says she doesn't want to date anyone older than 26, but often looks at ­profiles of thirty-somethings, Match will know she is in fact open to meeting older men. That is, the algorithm looks at the behaviour of similar users and factors in that ­information, too.

Until Ginsberg joined IAC, which owns Match, in 2006, she worked at i2 Technologies, a supply-chain management company, also based in Dallas.

"When you give it stimuli, it forms neural pathways," he says. It's learning as you go." The same principles are powering the recommendation engines at popular sites around the web.

Amazon uses similar ­technology to recommend new products for people to buy, Pandora learns from likes and dislikes to customise its internet radio stations, and Netflix famously offered $1m to anyone who could improve the effectiveness of its algorithm by 10 per cent.

||

Despite being in a mid-rise office tower overlooking a turnpike in the dry, landlocked city of Dallas, Texas, the Match offices are evocative of a racier environment, where anything might happen.So, if a woman says she doesn't want to date anyone older than 26, but often looks at ­profiles of thirty-somethings, Match will know she is in fact open to meeting older men. That is, the algorithm looks at the behaviour of similar users and factors in that ­information, too.Until Ginsberg joined IAC, which owns Match, in 2006, she worked at i2 Technologies, a supply-chain management company, also based in Dallas."When you give it stimuli, it forms neural pathways," he says. It's learning as you go." The same principles are powering the recommendation engines at popular sites around the web.Amazon uses similar ­technology to recommend new products for people to buy, Pandora learns from likes and dislikes to customise its internet radio stations, and Netflix famously offered $1m to anyone who could improve the effectiveness of its algorithm by 10 per cent.

m to anyone who could improve the effectiveness of its algorithm by 10 per cent.

Leave a Reply