MoviExplain: a recommender system with explanations

By Panagiotis Symeonidis, Alexandros Nanopoulos, Yannis Manolopoulos. 2009

132 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems

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Citations in the corpus

Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI (2019, neutral): “Doshi-Velez, 2017; Selvaraju et al., 2016; Sheh, 2017; Symeonidis, Nanopoulos, and Manolopoulos, 2009; Tapaswi et al., 2016; Williams et al., 2016).”
Knowledge-aware Autoencoders for Explainable Recommender Systems (2018, methodology): “In order to overcome this issue, new methods have been developed in order to generate an explainable recommendation ([18] provides an overview of the most successful approaches proposed over the years) such asMoviExplain [16], which exploits movies metadata to justify its recommendation”
Make it personal: A social explanation system applied to group recommendations (2017, background): “Research in explanations started with the premise that users would more likely trust a recommendation when they know the rationale behind it (Herlocker et al., 2000; Zanker and Ninaus, 2010; Lamche et al., 2014; Symeonidis et al., 2009).”
Explanation and Justification in Machine Learning : A Survey Or (2017, background): “[Symeonidis et al. , 2009] presented a style of justiﬁcation that focused on the most important feature along with the user’s past history with regards to that feature.”
Recommender systems survey (2013, methodology): “[214] they use a prototype ‘‘MoviExplain’’ to put into the test the research showed into Symeonidis et al.”
Interactive recommender systems: A survey of the state of the art and future research challenges and opportunities (2016, methodology): “(2015), SetFusion and TIGRS allow user intervention by using sliders to adjust the Figure 13: MoviExplain (Symeonidis et al., 2009) justifies the recommendations in a table that represents relationships between the rating history of the user and a feature of the recommended item.”
Human-grounded Evaluations of Explanation Methods for Text Classification (2019, background): “Secondly, if an AI’s performance is close to human intelligence, the explanations can increase humans’ confidence and trust in the AI (Symeonidis et al., 2009).”
How should I explain? A comparison of different explanation types for recommender systems (2014, background): “In the existing literature on recommender system explanations, authors often limit their analysis to some specific explanation goals [Herlocker et al., 2000; Bilgic and Mooney, 2005; Pu and Chen, 2006, 2007; Symeonidis et al., 2009] or explanation types [Vig et al., 2009; Gedikli et al., 2011].”
A generalized taxonomy of explanations styles for traditional and social recommender systems (2012, methodology): “3.2 Measuring effectiveness, justification, and preference In this section, we present a third user study and the second part of the user study reported in Symeonidis et al. (2009).”
User Preferences for Hybrid Explanations (2017, background): “To better understand user preferences, research has compared explanation styles [2], combined di erent explanation styles [24], and considered dimensions such as personalization [25, 26], tags [30], ranking [17], and natural language presentations [5].”