JUMPERS: Jobzippers User-driven Multi-relational and Personalized Recommender System - Texts

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Research unit

INNOSUISSE

Project number

12167.2;5 PFES-ES

Project title

JUMPERS: Jobzippers User-driven Multi-relational and Personalized Recommender System

Texts for this project

	German	French	Italian	English
Short description	-		-
Abstract	-		-

Inserted texts

Category	Text
Short description (English)	JUMPERS: Jobzippers User-driven Multi-relational and Personalized Recommender System
Short description (French)	JUMPERS: Jobzippers User-driven Multi-relational and Personalized Recommender System
Abstract (English)	This project aims at designing, validating and implementing a user-driven multi-relational and personalized recommender system for Jobzippers, the European career center network. The customers targeted by this system are university graduates and employers. The main commercial innovation lies in intelligent recommendation relying on contacts established by students and graduates during the course of their studies with professionals, professors, mentors and peers with similar profile and professional interests. The main scientific innovation lies in advanced multirelational and contextual recommendation algorithms.
Abstract (French)	This project aims at designing, validating and implementing a user-driven multi-relational and personalized recommender system for Jobzippers, the European career center network. The customers targeted by this system are university graduates and employers. The main commercial innovation lies in intelligent recommendation relying on contacts established by students and graduates during the course of their studies with professionals, professors, mentors and peers with similar profile and professional interests. The main scientific innovation lies in advanced multirelational and contextual recommendation algorithms.