{"id":313,"date":"2019-11-22T14:00:02","date_gmt":"2019-11-22T13:00:02","guid":{"rendered":"http:\/\/blogg.lnu.se\/disa\/?p=313"},"modified":"2019-11-21T15:24:39","modified_gmt":"2019-11-21T14:24:39","slug":"keynote-machine-learning-for-better-entertainment-recommendations-a-nordic-perspective","status":"publish","type":"post","link":"https:\/\/blogg.lnu.se\/disa\/?p=313","title":{"rendered":"Keynote: Machine Learning for better entertainment recommendations: A Nordic perspective"},"content":{"rendered":"

During this years\u00a0Big Data Conference at Linnaeus University on December 5-6 2019<\/a> we have several very interesting Keynote speakers, one of them is Antonina Danylenko, Head of Applied Machine Learning at The Nordic Entertainment Group who offers video-on-demand streaming, linear TV channels and radio broadcasting \u2013 probably best known for their Viaplay, Viafree & Viasat platforms.<\/p>\n

She will talk about<\/strong> how the entertainment industry is transforming at a rapid rate. This is driven by new trends, growing customer expectations and AI technologies allowing for more innovation, disruption and opportunities for growth. At the same time, the industry is getting increasingly crowded \u2013 as the use of streaming services is on the rise, and the Nordic region spends more time online than ever before. Nearly four out of ten people watch video content on a daily basis, with three-quarters of the 16-24 year-old age bracket streaming that content from subscription-based services. We are seeing a new phenomenon emerge known as \u2018stacking\u2019 behaviour \u2013 where households typically subscribe to more than one service, just to keep their options open when it comes to deciding what to watch. With so many options out there, people can be paralysed by what\u2019s known as the \u2018paradox of choice.\u2019 Personalising every aspect of the customer journey has become our main focus in the recommendation space, as consumers of entertainment have never been more spoilt for choice. Serving up relevant content recommendations at the right time is key to making the decision process as easy as possible. However, building and maintaining the lifecycle of recommender systems to capture customers’ behavior and use different algorithms to guide them towards something they will enjoy watching is not easy. In this presentation, I will outline the end-to-end process of building a recommender system utilising Big Data and Machine Learning to address this challenge.\u201d<\/p>\n

Don\u2019t miss out on the opportunity to listen to him and take part of the conference by\u00a0signing up here<\/a>\u00a0by November 25th.<\/p>\n

\"\"<\/p>\n

More about Antonina Danylenko<\/strong> who holds a PhD in Computer Science from Linnaeus University, Sweden where she wrote a dissertation on the topic of \u201cDecision Algebra: A General Approach to Learning and Using Classifiers\u201d. After several years working at IKEA within Solution Architecture and Data Science domains , she joined the Nordic Entertainment Group\u2014where she is now the Head of Applied Machine Learning. The Nordic Entertainment Group offers video-on-demand streaming, linear TV channels and radio broadcasting \u2013 probably best known for their Viaplay, Viafree & Viasat platforms. They\u2019re responsible for connecting over 1.4 million subscribers to the content they love, with more than 1900 employees across the Nordics and the UK.<\/p>\n","protected":false},"excerpt":{"rendered":"

During this years\u00a0Big Data Conference at Linnaeus University on December 5-6 2019 we have several very interesting Keynote speakers, one of them is Antonina Danylenko, Head of Applied Machine Learning at The Nordic Entertainment Group who offers video-on-demand streaming, linear TV channels and radio broadcasting \u2013 probably best known for their Viaplay, Viafree & Viasat […]<\/p>\n","protected":false},"author":401,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[93],"tags":[],"class_list":["post-313","post","type-post","status-publish","format-standard","hentry","category-blogg"],"acf":[],"yoast_head":"\r\nKeynote: Machine Learning for better entertainment recommendations: A Nordic perspective - DISA<\/title>\r\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\r\n<link rel=\"canonical\" href=\"https:\/\/blogg.lnu.se\/disa\/?p=313\" \/>\r\n<meta property=\"og:locale\" content=\"en_US\" \/>\r\n<meta property=\"og:type\" content=\"article\" \/>\r\n<meta property=\"og:title\" content=\"Keynote: Machine Learning for better entertainment recommendations: A Nordic perspective - 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