DEGARI 2.0: A diversity-seeking, explainable, and affective art recommender for social inclusion

Abstract

We present DEGARI 2.0 (Dynamic Emotion Generator And ReclassIfier): an explainable, affective-based, art recommender relying on the commonsense reasoning framework TCL and exploiting an ontological model formalizing the Plutchik’s theory of emotions. The main novelty of this system relies on the development of diversity-seeking affective recommendations obtained by exploiting the spatial structure of the Plutchik’s ‘wheel of emotion’. In particular, such development allows to classify and to suggest, to museum users, cultural items able to evoke not only the very same emotions of already experienced or preferred objects (e.g. within a museum exhibition), but also novel items sharing different emotional stances. The system’s goal, therefore, is to break the filter bubble effect and open the users’ view towards more inclusive and empathy-based interpretations of cultural content. The system has been tested, in the context of the EU H2020 SPICE project, on the community of deaf people and on the collection of the GAM Museum of Turin. We report the results and the lessons learnt concerning both the acceptability and the perceived explainability of the received diversity-seeking recommendations.