Content Discovery with Semantic Flows
Recommendation system is a powerful tool to increase customer engagement and satisfaction for media industry. Existing approaches rely on measuring similarity between contents and similarity between customers. In this talk, I am introducing Sky’s patent-pending machine learning research by looking at the recommendation problem from a different angle: what is the benefit of learning dissimilarity? In particular, I will introduce semantic flow, a brand-new approach, to measure semantic distance between concepts and its capability on recommending exciting and surprising contents to customers.
Jian Li is principal data scientist and data science team manager in Sky. His team focuses on machine learning research for Sky’s content discovery products and services including search, recommendation, data supply and enrichment. His team’s research covers various topics including deep learning, natural language processing, ranking, semantic knowledge inference and supply chain optimisation. Before joining Sky, Jian was with Microsoft Research in Cambridge where he developed the personalised email classification product for Microsoft Exchange and Office 365 services. This product has been used by 50 million customers globally by the end of 2015. Jian holds a Ph.D in computer vision and experimental psychology from University of Bristol.