Using Implicit Job Seeker Preferences to Improve Job Recommendation Ranking

This thesis focuses on the field of Job Recommendation. Particularly, we focus on using implicit preferences exhibited by the job seeker in interactions with a web platform to propose an improved ranking algorithm for a job recommendation platform called Magnet.me. We also study evaluation of relevance, and evaluation of recommendation sorting...

Ranking and Context-awareness in Recommender Systems

In this thesis we report the results of our research on recommender systems, which addresses some of the critical scientific challenges that still remain open in this domain. Collaborative filtering (CF) is the most common technique of predicting the interests of a user by collecting preference information from many users. In order to determine...