Titulo: Privacy-enabled scalable recommender systems
Asesores:
Harold Castro - Uniandes
Michel Riveill - Université de Nice
Presidente del Jurado: Frederic PRECIOSO - Université de Nice
Jurados:
Claudia Jiménez Guarin - Uniandes
Florent Masseglia - INRIA Sophia Antipolis
Breve Resumen
Recommendation systems are used by several online retailers, online content streaming services and social networking sites to improve the user's experience of their services by automatically filtering their content or offers of items to the ones most likely to interest the user.
It is critical for recommender systems to provide good recommendations even when the number of items or users increase in the system. On my thesis we focused on providing privacy to these systems, which has an impact on how good the recommendations are and how the systems react as the number of users, items and recommendation requests increase in the system. The tradeoff between these systems.
Fecha: Miércoles 10 de diciembre
Hora: 7:00 a.m.
Lugar:
Edificio Hermes - Sala de Producción Audiovisual
Universidad de los Andes
Carrera Primera # 18A-12