Heuristic evaluation of user interfaces
CHI '90 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Reconstructive expert system explanation
Artificial Intelligence
Fab: content-based, collaborative recommendation
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A personal news agent that talks, learns and explains
Proceedings of the third annual conference on Autonomous Agents
Content-based book recommending using learning for text categorization
DL '00 Proceedings of the fifth ACM conference on Digital libraries
Explaining collaborative filtering recommendations
CSCW '00 Proceedings of the 2000 ACM conference on Computer supported cooperative work
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A review of explanation methods for Bayesian networks
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Rule Based Expert Systems: The Mycin Experiments of the Stanford Heuristic Programming Project (The Addison-Wesley series in artificial intelligence)
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Proceedings of the 2007 ACM conference on Recommender systems
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Tagsplanations: explaining recommendations using tags
Proceedings of the 14th international conference on Intelligent user interfaces
A Survey of Explanations in Recommender Systems
ICDEW '07 Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering Workshop
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AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
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HUGIN: a shell for building Bayesian belief universes for expert systems
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
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Proceedings of the third ACM conference on Recommender systems
MoviExplain: a recommender system with explanations
Proceedings of the third ACM conference on Recommender systems
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Proceedings of the 19th international conference on World wide web
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Proceedings of the 19th international conference on World wide web
Providing Justifications in Recommender Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Guest editorial: special issue on a decade of mining the Web
Data Mining and Knowledge Discovery
Explaining neighborhood-based recommendations
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Trees for explaining recommendations made through collaborative filtering
Information Sciences: an International Journal
Knowledge-Based Systems
Proceedings of the Fifth International Conference on Management of Emergent Digital EcoSystems
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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.