Within creativity research, interest and capability in utilizing text-mining models to quantify
the Originality of participant responses to Divergent Thinking tasks has risen sharply over …

Explanations are important for users to make decisions on whether to take
recommendations. However, algorithm generated explanations can be overly simplistic and …

As recommender systems become increasingly important in daily human decision-making,
users are demanding convincing explanations to understand why they get the specific …

The mood of individuals in the workplace has been well-studied due to its influence on task
performance, and work engagement. However, the effect of mood has not been studied in …

Due to substantial scientific and practical progress, learning technologies can effectively
adapt to the characteristics and needs of students. This article considers how learning …

In crowdsourcing systems, tasks are distributed to networked people for completion. To
ensure the output quality, current crowdsourcing systems highly rely on redundancy of …

The cold-start problem is one of the critical challenges in personalized recommender
systems. A lot of existing work has been studied to exploit a user-item rating matrix as well …

The evaluation of recommender systems relies on user preference data, which is difficult to
acquire directly because of its subjective nature. Current recommender systems widely …

People capture photos, audio recordings, video, and more on a daily basis, but organizing
all these digital artifacts quickly becomes a daunting task. Automated solutions struggle to …

ABSTRACT A user's trust in recommendations plays a central role in the acceptance or
rejection of a recommendation. One factor that influences trust is the source of the …