Xavier Amatriain is VP of Engineering at Quora, where he leads the team building the best source of knowledge in the Internet. With over 50 publications in different fields, Xavier is best known for his work on Machine Learning in general, and Recommender Systems in particular. Before Quora, he was Research/Engineering Director at Netflix, where he lead the team building the famous Netflix Recommendation algorithms. Previously, Xavier was Research Scientist at Telefonica Research and Research Director at UCSB. He has also lectured at different universities both in the US and Spain and is frequently invited speaker at conferences, companies, or summer schools. He has served as General Chair for the ACM Recommender Systems conference in 2010, Track Chair for WWW 2016, and PC member for many conferences including most recently KDD 2016.

In 2006, Netflix announced a $1M prize competition to advance recommendation algorithms. The recommendation problem was simplified as the accuracy in predicting a user rating measured by the Root Mean Squared Error. While that formulation helped get the attention of the research community, it put the focus on the wrong approach and metric while leaving many important factors out, in particular the UI.

In this talk I will talk of the Netflix Prize as the past of recommendations. I will then describe the present from an industry perspective based on my personal experience at Netflix first and Quora now . I will describe the different components of modern recommender systems such as: personalized ranking, similarity, explanations, context-awareness, or search as recommendation. I will also review the usage of novel algorithmic approaches such as Factorization Machines, Restricted Boltzmann Machines, SimRank, Deep Neural Networks, or Listwise Learning-to-rank. We will see how those components and algorithmic approaches can be used to recommend not only movies, but also questions, answers, topics, or users.

But, most importantly, I will give many examples of prototypical industrial-scale recommender systems with special focus on the user interface and its interaction with the algorithms. It is clearly in the interface of the UI and the novel algorithms where the future of recommender systems lays.

Professor Elisabeth André is a Full Professor of Computer Science at Augsburg University, Germany, and Chair of the Research Unit Human-Centered Multimedia. She received her Diploma and Doctoral Degrees in Computer Science from Saarland University. Before joining Augsburg University, she has been working as a principal researcher at the German Research Center for Artificial Intelligence (DFKI) in Saar-brücken. In addition, she has held various visiting appointments,mostrecentlyaninvitedprofessorshipatUniversité Paris-Sud, France.

Elisabeth André has a long track record in multimodal human-machine interaction, embodied conversational agents, affective computing and social signal processing. She is on the editorial board of various renowned international journals, such as ACM Transactions on Intelligent Interactive Systems (TIIS), IEEE Transactions on Affective Computing (TAC), Journal of Autonomous Agents and Multi-Agent Systems (JAAMAS), and AI Communications. Currently, she is serving as a General Co-Chair of the 18th ACM International Conference on Multimodal Interaction (ICMI).

In 2007 Elisabeth André was nominated Fellow of the Alcatel-Lucent Foundation for Communications Research. In 2010, she was elected a member of the prestigious German Academy of Sciences Leopoldina, the Academy of Europe and AcademiaNet. She is also an ECCAI Fellow (European Coordinating Committee for Articial Intelligence).

Recent years have initiated a paradigm shift from pure task-based human-machine interfaces towards socially-sensitive interaction. In addition to what users explicitly say or gesture at, socially-sensitive interfaces are able to sense more subtle human cues, such as head postures and movements, to infer psychological user states, such as attention and affect, and also to enrich system responses with social signals. However, most approaches focus on offline analysis of previously recorded data limiting the investigation to prototypical behaviors in laboratory-like settings. In my presentation, I will focus on challenges that arise when integrating social signal processing techniques into interactive systems designed for real-world applications. From a technical perspective, this requires effective tools able to synchronize, process, and analyze relevant signals in online mode. From a user perspective, appropriate strategies need to be defined to respond to social signals at the right moment in time without disturbing the flow of interaction.

I will discuss two interaction styles for socially-sensitive interfaces. In the area of information retrieval, the concept of empathic stimulation has been used to optimize the selection and presentation of data. The basic idea is to exploit sensory data on the users’ emotional state to provide them with cues that inspire their curiosity during the data exploration task. In the domain of social coaching, the concept of social augmentation has been employed to give people ambient feedback on their behavior while being engaged in a social interaction. The presentation will be illustrated by examples from various national and international projects following these two interaction styles.