Personalizing Spotify Search By Learning To Rank - Marcus Isaksson скачать в хорошем качестве

Personalizing Spotify Search By Learning To Rank - Marcus Isaksson

At Spotify the main search mode is search-as-you-type where new results are presented after each character entered by the user. These partial search queries are inherently very ambiguous. For instance, when typing "e", the user might be looking for "Elvis", "Eminem" or something else. Without information about the user we have to resort to sorting artists based on popularity. However, we can do better. By mining user data and training a machine learning model to predict clickthrough logs, we have built a ranking model that personalizes the order of presented items and as a result achieves higher total success ratios in search sessions. In this talk Magnus gives an overview of the Learning to Rank problem and present some details of the current implementation for search at Spotify. #HyperightDataTalks is a video podcast of best presentations, discussions and interviews with some of the most innovative minds, enterprise practitioners, technology and service providers, start-ups and academics, working with Data Science, Data Management, Big Data, Analytics, AI, IOT and much more. All presentations are taken from Hyperight´s Data summits and now available for you. For more interviews, audio podcast and videos from some of the best presentations from our Data Summits, please visit http://www.hyperight.com Presentation recorded during: Nordic Data Science Summit 2016 - http://www.nordicdatasciencesummit.com/ Follow us on Twitter:   / datanordics   More information about Hyperight: http://www.hyperight.com/ Subscribe to our channel:    / @hyperight