Panel Discussion

Specific challenges for tourism recommender systems seen from the academic and the industry perspective

Panelists

• Neal Lathia (Skyscanner, UK): Neal Lathia is a Senior Data Scientist at Skyscanner in London. His work has always focused on data-driven applications; this journey has spanned digital health monitoring, smartphone sensors and applications, urban computing, online recommender systems, and the analysis of ‘big’ data sets of transport records, and financial transactions. He has a PhD and MSci in Computer Science, both from University College London: his PhD research introduced methods for evaluating collaborative filtering algorithms over time. Previously, he was, among others, an entrepreneur in Accelerate Cambridge at the Judge Business School, University of Cambridge, and a Senior Research Associate in the Computer Laboratory at the University of Cambridge. Neal Lathia is the keynote speaker of RecTour 2017.
• Markus Zanker (University of Bozen/Bolzano, Italy): Markus Zanker is a professor at the Faculty of Computer Science of the Free University of Bozen-Bolzano, Italy. Before, he was an associate professor at the Alpen-Adria-Universität Klagenfurt, Austria. He holds diploma degrees in Computer Science and in Business Administration and received his doctoral degree in technical sciences from Universität Klagenfurt in 2002. His research focuses on knowledge-based information systems supporting decision making processes such as personalized information filtering and retrieval, product recommendation, conversational sales advisor systems and product configurators. Research questions of his work target on fusing human domain expertise with inductively learned knowledge, decision making and the influence of decision biases as well as methodological questions about the evaluation of information systems. [Slides]
• David Zibriczky (trivago, Germany): David Zibriczky joined trivago as a data scientist in 2016. He focuses on designing real-time user profiling techniques in order to provide personalized recommendations for hotel search. He received his MSc in Computer Science and PhD in Business and Management at Budapest University of Technology and Economics. He gained several years of industrial experience in implementing and optimizing large-scale real-time recommender systems in various domains (traveling, IPTV, streaming media, news and coupon sites). He is also interested in conducting scientific research in recommender systems, focusing on factorization techniques, scalable nearest-neighbor algorithms, ensemble methods, user profiling and explanation of recommendations. [Slides]