摘要：Recommenders are ubiquitous on the Internet today: they tell you which book to read, which movie you should watch, predict your next holiday destination, give you advices on restaurants and hotels, they are even responsible for the posts that you see on your favorite social media and potentially greatly influence your friendship on social networks. While many approaches exist, collaborative filtering is one of the most popular approaches to build online recommenders that provide users with content that matches their interest. Interestingly, the very notion of users can be general and span actual humans or software applications. Recommenders come with many challenges beyond the quality of the recommendations. One of the most prominent ones is their ability to scale to a large number of users and a growing volume of data to provide real-time recommendations introducing many system challenges. Another challenge is related to privacy awareness: while recommenders rely on the very fact that users give away information about themselves, this potentially raises some privacy concerns. In this talk, I will focus on the challenges associated to building efficient, scalable and privacy-aware recommenders.
关键词：Recommenders; Collaborative filtering; Distributed systems
