Multi-Round Recommendations for Stable Groups

Abstract

Recommender systems have been used for suggesting the most suitable products and services for users in diverse scenarios. More recently, the need for making recommendations for groups of users has become increasingly relevant. In addition, there are applications in which recommendations are required in a consecutive sequence. Group recommendations present a challenge for recommender systems: how to balance the preferences of the individual members of a group. On the other hand, when making recommendations for a group for multiple rounds, a recommender has a possibility to dynamically try to balance the preference differences between the group members. This paper introduces two novel methods for multi-round group recommendation scenarios: the adjusted average aggregation method and the average-min-disagreement aggregation method. Both methods aim to provide a group with highly relevant results for the group, while remaining fair for all group members. We experimentally evaluate our approach for groups with different characteristics and show that our methods outperform baseline solutions in all scenarios.