An approach for explaining group recommendations based on negotiation information

CHRISTIAN VILLAVICENCIO; SILVIA SCHIAFFINO; J. ANDRES DIAZ PACE; ARIEL MONTESERIN

IAES International Journal of Artificial Intelligence

IAES

Año: 2022

2089-4872

Explaining group recommendations has gained importance over the last years. Although the topic of recommendation explanation has received attention in the context of single-user recommendations, only a few Group Recommender systems (GRS) currently provide explanations for their group recommendations. However, those GRS that support explanations, provide either explanations being highly reliant on the aggregation technique used for generating the recommendation (most of them trying to tackle shortcomings of the underlying technique), or explanations with a rich content but requiring users to provide considerable additional data. In this article, we present a novel approach for providing explanations of group recommendations, which are generated by a GRS based on multi-agent negotiation techniques. An evaluation of our approach with a user study in the movies domain has shown promising results. Explanations provided by our GRS system helped users during the decision-making process, since they modified the feedback given to recommended items. This is an improvement with respect to systems that do not provide explanations for their recommendations.