News Recommendations at scale at Bloomberg Media: Challenges and Approaches

Abstract

In the past decade, news consumption through traditional channels such as print has been on the decline while online and digital news consumption has been steadily growing. Bloomberg, renowned for its products in the financial world, has a very strong presence in the news and media industry. Bloomberg Media, on an average, publishes 400-500 stories and videos per day and we have close to 30 million unique visitors on our websites and mobile applications every month consuming this content. At such a scale it is very important to recommend relevant information for a good user experience. Recommendations in the News and Media domain bring a unique set of challenges due to the dynamic nature of the data as well as unique consumption patterns. The biggest challenge with building recommendation systems in the News domain is the dynamic nature of the domain itself; new content is published every few minutes and majority of the content has a short shelf life, i.e., the news is not relevant to users after a certain time span and the time span is generally of the order of hours rather than days, making it important to deliver relevant content in a timely manner. Moreover, our users consume content differently based on time of day. For example, some users whose focus is market news and market data during the day consume more long form and generic articles and videos in the evening. User preferences, along with being cyclical in nature, tend to change over time, so algorithms need to adapt to the changing taste of the user. In addition, we need to ensure that the users do get their share of important/trending news and are not put into a filter bubble. In this talk, we will present some novel techniques we have applied to popular approaches in the field of Recommender Systems to be able to address the unique challenges which the news domain presents.