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

Abstract

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 relationships between domains. But when a new domain is added to a cross-domain recommender system, this knowledge usually lacks and giving cross-domain recommendations is not a trivial problem anymore. Current approaches uses content-based relations to bootstrap new domains in cross-domain recommendations. In this thesis we propose a new model that transfers existing users’ preference based relations between domains from an auxiliary Social Web system to a cross-domain recommender system in which a new domain needs to be bootstrapped. In a case study on the Open Images dataset we researched this solution to get insight in how well the model works and whether it has potential for widespread usage.