Review-based recommender systems (RS) have shown great potential in helping users manage information overload and find suitable items. However, a lack of trust still impedes the widespread acceptance of RS. To increase users’ trust, research proposes various methods to generate justifications or explanations. Furthermore, online customer reviews (OCRs) are found to be a trustworthy and reliable source of information. However, it is still unclear how justifications compare to explanations in their influence on users’ trust and whether basing them on OCRs additionally adds trust. Hence, we conduct an online experiment with 531 participants and find that explanations exceed justifications in increasing users’ trust, while basing them on OCRs d...
Explainable artificial intelligence (XAI), known to produce explanations so that predictions from AI...
Recommender systems assist users in decision-making, where the presentation of recommended items and...
Our increasing reliance on complex algorithms for recommendations calls for models and methods for e...
This report discusses the explanations in the domain of recommender systems: A review of the researc...
A significant challenge for recommender systems (RSs), and in fact for AI systems in general, is the...
A few Recommender Systems (RS) resort to explanations so as to enhance trust in recommendations. How...
The Thirty-First International FLAIRS Conference (FLAIRS-31), Florida, United States of America, 21-...
As AI systems become ever more intertwined in our personallives, the way in which they explain thems...
Recommender systems that incorporate a social trust network among their users have the potential to ...
With the increasing demand for predictable and accountable Artificial Intelligence, the ability to e...
Online customer reviews have been shown to have a powerful impact on the sales of a given product or...
Recommender systems are ubiquitous in most of our interactions in the current digital world. Whether...
More and more aspects of our everyday lives are influenced by automated decisions made by systems th...
A recommender system's ability to establish trust with users and convince them of its recommendation...
Modern recommender systems face an increasing need to explain their recommendations. Despite conside...
Explainable artificial intelligence (XAI), known to produce explanations so that predictions from AI...
Recommender systems assist users in decision-making, where the presentation of recommended items and...
Our increasing reliance on complex algorithms for recommendations calls for models and methods for e...
This report discusses the explanations in the domain of recommender systems: A review of the researc...
A significant challenge for recommender systems (RSs), and in fact for AI systems in general, is the...
A few Recommender Systems (RS) resort to explanations so as to enhance trust in recommendations. How...
The Thirty-First International FLAIRS Conference (FLAIRS-31), Florida, United States of America, 21-...
As AI systems become ever more intertwined in our personallives, the way in which they explain thems...
Recommender systems that incorporate a social trust network among their users have the potential to ...
With the increasing demand for predictable and accountable Artificial Intelligence, the ability to e...
Online customer reviews have been shown to have a powerful impact on the sales of a given product or...
Recommender systems are ubiquitous in most of our interactions in the current digital world. Whether...
More and more aspects of our everyday lives are influenced by automated decisions made by systems th...
A recommender system's ability to establish trust with users and convince them of its recommendation...
Modern recommender systems face an increasing need to explain their recommendations. Despite conside...
Explainable artificial intelligence (XAI), known to produce explanations so that predictions from AI...
Recommender systems assist users in decision-making, where the presentation of recommended items and...
Our increasing reliance on complex algorithms for recommendations calls for models and methods for e...