A generalized taxonomy of explanations styles for traditional and social recommender systems |
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Authors: | Alexis Papadimitriou Panagiotis Symeonidis Yannis Manolopoulos |
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Affiliation: | 1.Department of Informatics,Aristotle University,Thessaloniki,Greece |
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Abstract: | Recommender systems usually provide explanations of their recommendations to better help users to choose products, activities
or even friends. Up until now, the type of an explanation style was considered in accordance to the recommender system that
employed it. This relation was one-to-one, meaning that for each different recommender systems category, there was a different
explanation style category. However, this kind of one-to-one correspondence can be considered as over-simplistic and non generalizable.
In contrast, we consider three fundamental resources that can be used in an explanation: users, items and features and any
combination of them. In this survey, we define (i) the Human style of explanation, which provides explanations based on similar
users, (ii) the Item style of explanation, which is based on choices made by a user on similar items and (iii) the Feature
style of explanation, which explains the recommendation based on item features rated by the user beforehand. By using any
combination of the aforementioned styles we can also define the Hybrid style of explanation. We demonstrate how these styles
are put into practice, by presenting recommender systems that employ them. Moreover, since there is inadequate research in
the impact of social web in contemporary recommender systems and their explanation styles, we study new emerged social recommender
systems i.e. Facebook Connect explanations (HuffPo, Netflix, etc.) and geo-social explanations that combine geographical with
social data (Gowalla, Facebook Places, etc.). Finally, we summarize the results of three different user studies, to support
that Hybrid is the most effective explanation style, since it incorporates all other styles. |
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