Retail Startup Automator: Difference between revisions
(Created page with "Client: Harry Collard, The Hut Group <Harry.Collard@thehutgroup.com> Retail Startup Automator Online retailers invest heavily in reformatting information from product ca...") |
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Client: Harry Collard, [[The Hut Group]] <Harry.Collard@thehutgroup.com> | Client: Harry Collard, [[The Hut Group]] <Harry.Collard@thehutgroup.com> | ||
Online retailers invest heavily in reformatting information from product catalogues into their own databases, and this is a barrier to entry for new businesses. But it should be possible to automate the startup process, by using artificial intelligence to extract the common data structure for standard products that get sold by dozens of different retailers. You must first create a machine learning algorithm that can automatically infer page templates by comparing product detail pages for the same product being sold on a variety of existing retail sites. Those templates can then be applied to extract the semi-structured data from retail sites for further products that have not been seen in the training phase. The resulting database will then be automatically published as a brand new shopping site, offering instant startup at the press of a button, allowing specialist entrepreneurs to cater for niche retail markets such as toothpaste-compare.com, muesli-and-sandal-world, or toysforhamsters. | Online retailers invest heavily in reformatting information from product catalogues into their own databases, and this is a barrier to entry for new businesses. But it should be possible to automate the startup process, by using artificial intelligence to extract the common data structure for standard products that get sold by dozens of different retailers. You must first create a machine learning algorithm that can automatically infer page templates by comparing product detail pages for the same product being sold on a variety of existing retail sites. Those templates can then be applied to extract the semi-structured data from retail sites for further products that have not been seen in the training phase. The resulting database will then be automatically published as a brand new shopping site, offering instant startup at the press of a button, allowing specialist entrepreneurs to cater for niche retail markets such as toothpaste-compare.com, muesli-and-sandal-world, or toysforhamsters. | ||
Latest revision as of 08:56, 9 November 2016
Client: Harry Collard, The Hut Group <Harry.Collard@thehutgroup.com>
Online retailers invest heavily in reformatting information from product catalogues into their own databases, and this is a barrier to entry for new businesses. But it should be possible to automate the startup process, by using artificial intelligence to extract the common data structure for standard products that get sold by dozens of different retailers. You must first create a machine learning algorithm that can automatically infer page templates by comparing product detail pages for the same product being sold on a variety of existing retail sites. Those templates can then be applied to extract the semi-structured data from retail sites for further products that have not been seen in the training phase. The resulting database will then be automatically published as a brand new shopping site, offering instant startup at the press of a button, allowing specialist entrepreneurs to cater for niche retail markets such as toothpaste-compare.com, muesli-and-sandal-world, or toysforhamsters.
