Making Sense of Fast Big Data (DAIS Keynote)

摘要

Computing systems that make human sense of big data, usually called personalization systems or recommenders, and popularized by Amazon and Netflix, essentially help Internet users extracting information of interest to them. Leveraging machine learning techniques, research on personalization has mainly focused on improving the quality of the information extracted, according to some measure of quality. Yet, building an operational recommender goes far beyond, especially in a world where data is not only big but also changes very fast. This talk will discuss system challenges to scale to a large number of users and a growing volume of fastly changing data to eventually provide real-time personalization.