Company

Scarab Research provides highly personalized recommendations for customers of e-commerce businesses. Our machine-learning technology analyses user behavior to generate individually relevant product offers for consumers to make the most of their shopping experience. Scarab personalization technology is proven to increase top and bottom line growth by driving product conversion and brand loyalty.

Our online sales tools (Product Recommender, Personalized Display Ads) integrate to any system in a matter of hours so clients typically see actual results in just a few weeks.

Scarab Research was established in 2009 in Budapest, Hungary and today reaches millions of users with personalized recommendations on a daily basis, for a variety of e-commerce businesses on a global scale. We pride ourselves in our ability to combine your data with the latest machine-learning technologies to quickly create measurable added value. Our clients include established market leading online retailers and advertisers from Europe and the USA.

In December 2013, Scarab Research was acquired by Emarsys, the global customer engagement solutions provider. Emarsys has now integrated Scarab Research’s predictive recommendations technology into its platform to provide an “all-in-one” powerful and comprehensive digital marketing solution.

Management Team

Viktor Szathmáry // CEO

Viktor is an entrepreneur and software developer, co-founder and CEO of Scarab Research. He was lead developer for New York-based e-commerce startup FreshDirect (2001-2005). Other notable projects include: Concorde Direct, the first Hungarian online stock trading system (1999-2001); chat.hu, the largest Hungarian chat community, with over a million registered accounts (1999-2001).

Dániel Fogaras, Ph.D. // Chief Science Officer

Dániel holds a PhD in Computer Science from the Budapest University of Technology and Economics. He specialized in scalable data mining algorithms and introduced new techniques for retrieving related web pages from the hyper-link structure of the web. He started his professional carrier by joining Google Inc., Mountain View, CA in 2006 and transferred to Google Switzerland in 2008. He contributed to the AdWords and AdSense products as a software engineer, researcher and project leader. He developed new components and initiated projects for fighting click fraud and for targeting ads. He is experienced both in off-line logs-analysis infrastructure and in on-line serving systems. He returned to Budapest and joined Scarab Research in 2010.

Advisory Board

Stan Matwin, Ph.D.

Stan Matwin is a professor at the School of Information Technology and Engineering, University of Ottawa, where he directs the Text Analysis and Machine Learning (TAMALE) lab. His research is in machine learning, data mining, and their applications, as well as in technological aspects of Electronic Commerce. Author and co-author of 150 research papers, he has worked at universities in Canada, the U.S., Europe and Latin America, where in 1997 he held the UNESCO Distinguished Chair in Science and Sustainable Development. Founding Director of the IT Cluster of the Ontario Research Centre for Electronic Commerce. Chair of the NSERC Grant Selection Committee for Computer Science and member of the Board of Directors of Communications and Information Technology Ontario (CITO).

Csaba Szepesvári, Ph.D.

Csaba Szepesvári received his PhD in 1999 from Jozsef Attila University, Hungary. He is currently an Associate Professor at the Department of Computing Science of the University of Alberta and a principal investigator of the Alberta Ingenuity Center for Machine Learning. Previously he held a senior researcher position at the Computer and Automation Research Institute of the Hungarian Academy of Sciences, where he headed the Machine Learning Group. Before that spent 5 years in the software industry. From 1998 he served as Research Director for Mindmaker, Inc., working on natural language processing and speech products, and became Vice President of Research in 2000. He is the coauthor of a book on nonlinear approximate adaptive controllers, published over 70 journal and conference papers and serves regularly on the program committee of various AI and machine learning conferences. His areas of expertise include machine learning, Markovian decision processes and nonlinear control.

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