With the recent advances in the field of artificial intelligence, an increasing number of
decision-making tasks are delegated to software systems. A key requirement for the success …

In recent years, a variety of review-based recommender systems have been developed, with
the goal of incorporating the valuable information in user-generated textual reviews into the …

Explanations have a large effect on how people respond to recommendations. However,
there are many possible intentions a system may have in generating explanations for a …

Recommender systems aim at predicting users' preferences based on abundant information,
such as user ratings, demographics, and reviews. Although reviews are sparser than ratings …

Explainable recommendation has shown its great advantages for improving
recommendation persuasiveness, user satisfaction, system transparency, among others. A …

In popular applications such as e-commerce sites and social media, users provide online
reviews giving personal opinions about a wide array of items, such as products, services …

The explanation interface has been recognized important in recommender systems as it can
help users evaluate recommendations in a more informed way for deciding which ones are …

The explanation interface has been recognized as important in recommender systems
because it can allow users to better judge the relevance of recommendations to their …

Social networking sites (SNS), which allow users to express opinions on products/services,
have become an important channel and platform for enterprises to acquire and trace users' …

Despite the potential impact of explanations on decision making, there is a lack of research
on quantifying their effect on users' choices. This paper presents an experimental protocol …