Challenges in Recommendation Systems at Twitter Scale
Twitter has amazing and unique content that is generated at an enormous velocity internationally. A constant challenge is how to find the relevant content for users so that they can engage in the conversation. Approaches span collaborative filtering and content based recommendation systems for different use cases. This talk gives insight into unique recommendation system challenges at Twitter’s scale and what makes this a fun and challenging task.
Ashish manages the Explore and Trends engineering teams at Twitter. He focusses on building scalable ML & recommendation systems. Prior to that, he was a Senior Director of Data Science at Capital One. He used AI/ML to generate insights from vast amounts of data and build interesting B2B, B2C and Enterprise products. Previously, he co-founded GALE Partners and headed the Machine Learning group, building AI/ML based marketing automation products. He helped the company grow from 9 to 120 in 2 years and setup the India office. He has over 19 years of experience in the technology industry, an MBA from Kellogg School of Management and a B Tech from IIT BHU.