DualDiv: diversifying items and explanation styles in explainable hybrid recommendation

In recommender systems, item diversification and explainable recommendations improve users' satisfaction. Unlike traditional explainable recommendations that display a single explanation for each item, explainable hybrid recommendations display multiple explanations for each item and are, therefore, more beneficial for users. When multiple explanations are displayed, one problem is that similar sets of explanation styles (ESs) such as user-based, item-based, and popularity-based may be displayed for similar items. Although item diversification has been studied well, the question of how to diversify the ESs remains underexplored. In this paper, we propose a method for diversifying ESs and a framework, called DualDiv, that recommends items by diversifying both the items and the ESs. Our experimental results show that DualDiv can increase the diversity of the items and the ESs without largely reducing the recommendation accuracy.