Françoise Soulié Fogelman – Attributed Social Networks for recommender systems
Social Networks can be used to model linked data and help dealing with non-independent data (non i.i.d.), which is a critical issue in data mining. Such a model allows deriving new features useful for data mining models and new ideas for more performant algorithms.
I will describe this approach on two cases. In recommender systems, attributed social networks can help building hybrid Top-N recommenders; the issue of large dimensionality leads us to consider new Top-N techniques. In internet credit card fraud detection, we will show how social networks can be used to represent the cards transactions; we will extract social network-based features and will demonstrate a very large increase in performances.
Françoise Soulié Fogelman has over 40 years’ experience in data mining, social network analysis and big data both in academia and industry. A former graduate from École Normale Supérieure, she holds a PhD from University of Grenoble. She was Professor at the University of Paris 11-Orsay, where she was advisor to 20 PhDs. She then worked in industry, as head of data mining and CRM business unit; at KXEN, she has been Vice President Innovation until the company was bought out by SAP; finally she joined Institut Mines Télécom where she worked for the Big Data platform TeraLab. She is presently Professor with the School of Computer Software at Tianjin University (China), head of the Data Science team.
She has co-authored more than 140 scientific publications and 13 books. She is an expert for the European Commission, ANR (French National Research Agency), French Competitivity cluster Cap Digital and CCF Big Data Task Force (China). She has organized many scientific conferences; she was chairman for KDD’09 in Paris and was awarded ACM Recognition of Service for her contribution.