Proceedings Abstracts of the Twenty-Third International Joint Conference on Artificial Intelligence

A Case-Based Solution to the Cold-Start Problem in Group Recommenders / 3042
Lara Quijano-Sánchez, Derek Bridge, Belén Díaz-Agudo, Juan Antonio Recio-García

In this paper we offer a potential solution to the cold-start problem in group recommender systems. To do so, we use information about previous group recommendation events and copy ratings from a user who played a similar role in some previous group event. We show that copying in this way, i.e. conditioned on groups, is superior to copying nothing and also superior to copying ratings from the most similar user known to the system.