(Updated) Graph Databases for Dynamic Recommender Systems and Outlier Detection

Interesting ideas  Slides and recording now at link below:

Talk by: Denis Vrdoljak & Gunnar Kleemann  at Cisco & UC Berkeley

“Graph Databases for Dynamic Recommender Systems and Outlier Detection”

Abstract: At Berkeley Data Science Group (BDSG), we set out to commercialize Data Science projects and ideas developed at UC Berkeley. From our first project (predicting biotech IPO’s) to our most recent work (AI assisted copywriting and NLP), we’ve developed several tools and techniques using Graph Databases to solve several of the challenges encountered. In this presentation, we’ll share some of the lessons we’ve learned and some of the techniques we’ve used.

Denis Vrdoljak: Denis is a Data Scientist/Marketing Analyst at Cisco and also a co-founder of Berkeley Data Science Group. His current work and research focus is in predicting edges in network graphs for recommender systems, and in using graph topologies to identify outliers for security.

Gunnar Kleemann, Ph.D.: Gunnar is a Data Science Instructor at UC Berkeley and co-founder of Berkeley Data Science Group. His current research focus and interest lies in analyzing collaboration networks of research scientists to identify patterns in community topologies and to identify outliers. He is currently analyzing geographic and domains.
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Slides and Recording are here: http://cognitive-science.info/community/weekly-update/

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