Effective explanations of recommendations: user-centered design

By Nava Tintarev, Judith Masthoff. 2007

195 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Information Systems

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In Information Systems

Citations in the corpus

The effects of recommendations’ presentation on persuasion and satisfaction in a movie recommender system (2010, background): “Tintarev and Masthoff [34, 36] researched the factors that subconsciously affect users when describing or evaluating a movie.”
Tagsplanations: explaining recommendations using tags (2009, background): “As discussed in [15], a shortcoming of item-based explanations is that users may not understand the connection between the explaining items and the recommended item.”
Knowledgeable Explanations for Recommender Systems (2010, background): “and others [4], [5], [6], [7] discussed the following set of characteristics that can be associated with explanations:”
Recommendation Diversification Using Explanations (2009, background): “Recommender Systems: The need for explaining recommendations is discussed in [8] and [10].”
Automatic Generation of Natural Language Explanations (2017, background): “However, it is well established by [25] that a good explanation must be clear, and interesting to the target user, since this information has a significant influence on the user’s decision.”
Recommender systems: from algorithms to user experience (2012, background): “Tintarev and Masthoff (2007) describe three motivations for explanations in recommender systems: (1) transparency, which shows the user how the recommendation was formed, so the user knows how much to trust it; (2) trust, which encourages the user to take the recommendation independent of how…”
The effects of transparency on trust in and acceptance of a content-based art recommender (2008, background): “Tintarev and Masthoff (2007) provide a number of guidelines for recommender system explanations.”
Tell me more?: the effects of mental model soundness on personalizing an intelligent agent (2012, background): “Prior work has focused on how an intelligent agent can explain itself to end users [9,13,15,22,27,28] , and how end user s might act upon such explanations to debug their intelligent agents [1,11,14,16,24].”
Explanations of recommendations (2007, methodology): “In addition, we found that this personalization should take the viewing context and mood into account (see [20] for more results from our focus groups).”
How should I explain? A comparison of different explanation types for recommender systems (2014, background): “Tintarev and Masthoff [2007b], for example, conclude that high transparency may impede efficiency as the users need more time to inspect an explanation type.”
A Theoretical Framework for Conversational Search (2017, background): “Effectiveness of a conversational system would likely depend on the system saying why it made a specific recommendations [35].”
Explaining Recommendations: Design and Evaluation (2015, background): “The presence of longer descriptions of individual items has been found to be positively correlated with both the perceived usefulness [75], and ease of use of the recommender system [68].”
Collaborative Filtering Recommender Systems (2011, background): “A later focus group study suggested that explanations canhelpwithuser satisfactionbyhelping them better know what to expect [145].”
Reinforcement Learning based Recommender Systems: A Survey (2021, background): “Explanation about recommendations made could improve user experience, boost their trust in the system, and help them make better decisions [148, 149, 150].”