Exploring Exploration in Recommender Systems: Where? How Much? For Whom?

Recommender systems focus on automatically surfacing suitable items for users from digital collections that are too large for the user to oversee themselves. A considerable body of work exists on surfacing items that match what a user liked in the past; this way, the recommender system will exploit its knowledge of a user's comfort zone. However...

A behaviour driven recommender system in the fashion domain

Web shops use recommender systems to help users find the products they find interesting in the large amount of available products online. An often used approach to do so is collaborative filtering. This method relies on historical user-item interactions and uses them to recommends products other users found interesting. Fashion is very reliant...

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...