Finding Friends and Lovers
Luiz Augusto Pizzato (Sydney University)
NICTA SML SEMINARDATE: 2011-09-08
TIME: 11:00:00 - 12:00:00
LOCATION: NICTA - 7 London Circuit
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ABSTRACT:
In this talk, I will give an overview of the work on personalisation done at the University of Sydney. I will particularly focus on our work on reciprocal recommenders, and on our experiments using online dating data.
Reciprocal recommenders for online dating deal with the problem of finding the best matches for online dating users. We have investigated different solutions to this problem, including content-based recommenders, collaborative filtering and hybrid recommenders. In this presentation, I will summarise our current research and describe some future directions of our personalisation project.
BIO:
Luiz is a postdoctoral researcher at the University of Sydney with interests in recommender systems, social network analysis, data mining and natural language processing.
Luiz received his Bachelor of Computer Science in 2000 from the Pontifical Catholic University of Porto Alegre (PUCRS), Brazil. In the same year, he joined the Hewlett Packard/PUCRS Research Centre in High Performance Computing. In 2003, Luiz received a Master of Computer Science from PUCRS for his thesis involving query expansion using thesauri information for information retrieval (IR). In 2003 at the University of Avora, Portugal, Luiz integrated his Masters research with the SINO search engine to enable the online search of legal decisions made by the Portuguese Attorney General. In 2009 at Macquarie University, Australia, Luiz was awarded his PhD for his work on the use the using linguistically motivated features in the document retrieval stages of the question answering task.
In 2009, Luiz joined the CHAI research group at the University of Sydney and the Smart Services CRC on the personalisation project. Since then, Luiz has been working in the personalisation domain with strong focus on data mining and recommender systems on social networks. Luiz is currently applying his research to people recommenders in social networks for task such as finding best matches in online dating and matching job candidates with employers.
