RACE:GP – a Generic Approach to Automatically Creating and Evaluating Hybrid Recommender Systems

In recent years, recommender systems have become a fundamental part of our online experience. Users rely on such systems in situations with many potential choices, such as watching a movie on a streaming service, reading a blog post, or listening to a song. Traditionally, these systems use techniques such as collaborative filtering and content...

From Talents to Team: Matching supply and demand with algorithms

The globalizing economy with its new goods and services, knowledge spread, and competition for talent is an increasing complexity for organizations, which requires organizations to adapt more quickly. Organizations are essential to society, as people are more productive in groups. For their continuity, it is important that organizations...

Towards a Social Web based solution to bootstrap new domains in cross-domain recommendations

Most recommender systems recommend items from a single domain. However, usually users’ preferences span across multiple domains. Cross-domain recommender systems can successfully recommend items in multiple domains when there is knowledge about the user’s preferences for items in at least one of the domains and when there is knowledge about...