---
title: "A Case-Based Solution to the Cold-Start Problem in Group Recommenders"
authors:
  - "Lara Quijano Sánchez"
  - "Derek G. Bridge"
  - "Belén Díaz-Agudo"
  - "Juan Antonio Recio-García"
doi: ""
url: "https://ijcai.org/proceedings/2013/453"
pdf: "https://ijcai.org/proceedings/2013/0453.pdf"
venue: "Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence"
year: 2013
pages: "3042-3045"
track: ""
keywords:
---

# A Case-Based Solution to the Cold-Start Problem in Group Recommenders

Lara Quijano Sánchez, Derek G. Bridge, Belén Díaz-Agudo, Juan Antonio Recio-García

*Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence*, pages 3042-3045.

## Abstract

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.