The term low-rank matrix factorisation is primarily found in the fields of Artificial Intelligence, Big Data and Smart Data, as well as Industry and Industry 4.0. It is a method that can be used to simplify and present large amounts of data in a clear and organised manner.<\/p>\n
Imagine a huge Excel spreadsheet with thousands of rows and columns, for example, containing customer data, production figures, or user behaviour. This spreadsheet often contains a lot of empty fields or superfluous information. Low-rank matrix factorization automatically decomposes this large spreadsheet into smaller, simpler structures, making patterns and correlations easier to identify.<\/p>\n
A vivid example is personalised product recommendations in online retail: here, the previous purchases and interests of many users are stored in a large matrix. With low-rank matrix factorization, the computer can recognise \u201ehidden\u201c preferences and trends to display targeted, suitable recommendations. This benefits both companies and customers through more focused offers and improved service.<\/p>\n
In short: Low-rank matrix factorisation helps to make complex datasets understandable and usable.<\/p>","protected":false},"excerpt":{"rendered":"