The Effectiveness of Personalized Movie Explanations: An Experiment Using Commercial Meta-data

By Nava Tintarev, Judith Masthoff. 2008

45 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Management Science and Operations Research

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Citations in the corpus

Explainable Recommendation: A Survey and New Perspectives (2018, background): “, 2019a), movies (Herlocker et al., 2000; Nanou et al., 2010; Tintarev and Masthoff, 2008), or videos (Toderici et al.”
On the Use of Opinionated Explanations to Rank and Justify Recommendations (2016, background): “Herewe focus on generating explanations that are feature-based and personalised (see also (Tintarev and Masthoff 2008)), highlighting features that are likely to matter most to the user.”
How should I explain? A comparison of different explanation types for recommender systems (2014, background): “In recent years, the concept of explanations has been widely discussed in the area of recommender systems [Pu and Chen, 2007; Tintarev and Masthoff, 2008; Vig et al., 2009; Friedrich and Zanker, 2011; Tintarev and Masthoff, 2012].”
Make it personal: A social explanation system applied to group recommendations (2017, methodology): “One typical method of measuring it, is asking users directly if they liked the explanation and to which extent (Tintarev and Masthoff, 2008).”
The effects of recommendations’ presentation on persuasion and satisfaction in a movie recommender system (2010, background): “The use of movie poster/ DVD cover provides additional information, which is significant for users when evaluating a recommended movie [32, 38].”
Visualization of Explanations in Recommender Systems (2014, background): “The presentation of the recommended item is one of the factors that may impact explanation effectiveness [13].”
Great Explanations: Opinionated Explanations for Recommendations (2015, background): “Here we focus on generating explanations that are feature-based and personalized (see also [17]), highlighting features that are likely to matter most to the user.”
Evaluating the effectiveness of explanations for recommender systems (2012, methodology): “Experiment 1 has been reported in full in Tintarev and Masthoff (2008b), and is therefore only summarized here.”
Explanation-based Ranking in Opinionated Recommender Systems (2018, background): “They are personalised for the target user by highlighting item features (pros and cons) that are likely to matter to the user (see also [12]) and that distinguish the item from other recommendations.”