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Evolving area
When technology changes so fast, so does the science that supports the technology. ‘We get phone calls all the time saying, “Hi I am doing my thesis on recommendation engines, and I’m re-writing algorithms!”’ says Andy Semple, Chief Commercial Officer of The Filter. ‘Every college kid seems to be doing their thesis on this! It’s an evolving area.’

‘Ahead of his time’
The Filter, which is based in Bath in the west of England, has created software which aims to deliver targeted music and film recommendations to users. It has received attention because of the sophisticated algorithm it has developed, and because of the background of one of its investors, musician Peter Gabriel. ‘The interesting thing about Peter Gabriel,’ says Semple, ‘is that he has always been involved in things early. In the 80s he was putting his music out on CD ROMS when people didn’t even have disc players on their computers. He brought together music and technology in his output when that was way ahead of its time. What is also highly relevant is that he invested in something called OD2, which predated iTunes, as a digital download store.’

The company believes its recommendation engine is superior because of the broad range of activity it is based on, including purchases, searches, music or films listened to, or watched. ‘The technology,’ says Semple, ‘is based on Bayesian probability maths. Within our team of 18 people we have four or five highly qualified mathematicians and artificial intelligence experts.’

screenshot from official website © TheFilter.comNeural networks
Their technology has been licensed for use in the Nokia music store and, though Semple can’t disclose names, they are picking up clients in the United States. ‘We are working with one of the four major music labels,’ says Semple, ‘and with one of the four major network TV companies.’ The mathematicians at The Filter ‘are looking at things like neural networks, which is another way of clustering content,’ says Semple. A neural network is a mathematical model that aims to find patterns in raw data. ‘Businesses have got too much data, and we can aggregate that data into neural networks. We can classify and work out connections between that data, and using algorithms, work out and fine tune the results.’
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