Oudemanhuispoort, Amsterdam, Netherlands

Past ADS Event: Coffee & Data on Personalisation & Recommendations

Room C0.17, Oudemanhuispoort 4-6, Amsterdam

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Access to information is increasingly being shaped by personalised recommendations. People worry about tunnel vision and being caught in a filter bubble. In this ADS Coffee & Data we will address Personalisation and Recommendations in the development of algorithms, and the implications on society of the surge in personalisation.

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08:45 – 09:00 Welcome reception with coffee

09:00 – 09:05 Introduction by Maarten de Rijke (Informatics Institute, UvA)

09:05 – 09:35 Julia Kiseleva (TU/e & UserSat) Understanding & Predicting User Satisfaction on Mobile Devices

09:35 – 10:05 Anne Schuth (Blendle) Recommending News

10:05 – 10:20 Coffee break

10:20 – 10:50 Xinyi Li (Informatics Institute, UvA) Personalisation & Academic Search

10:50 – 11:20 Balazs Bodo (Institute for Information Law, UvA) Personalised Communication from a User’s Perspective

11:20 – 11:30 Open discussion & wrap-up

11:30 – Drinks and snacks

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Amsterdam Data Science accelerates data science research by connecting, sharing and showcasing world-class technology, expertise and talent from Amsterdam on a regional, national and international level. Our research enables business and society to better gather, store, analyse and present data in order to gain valuable insights and make informed decisions.

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ADS is an initiative of:

• Informatics Institute (IvI), Universiteit van Amsterdam (UvA)

• Amsterdam Business School and Amsterdam School of Economics, Faculty of Economics and Business, UvA

• Computer Science, Vrije Universiteit (VU) Amsterdam

• Hogeschool van Amsterdam (HvA)

• Centrum Wiskunde en Informatica (CWI)

We organise Coffee & Data meetings and seminars on cutting-edge research, technologies and best practices.

We invite you to:

• share the data challenges in your organisation with the ADS community

• discover innovative ways to apply data science across fields

• network and discuss leading issues with students and top data scientists