  Since recommender systems have been created and developed to automate the
recommendation process, users can easily consume their desired video content on
online platforms. In this line, several content recommendation algorithms are
introduced and employed to allow users to encounter content of their interests,
effectively. However, the recommendation systems sometimes regrettably
recommend inappropriate content, including misinformation or fake news. To make
it worse, people would unreservedly accept such content due to their cognitive
heuristic, machine heuristic, which is the rule of thumb that machines are more
accurate and trustworthy than humans. In this study, we designed and conducted
a web-based experiment where the participants are invoked machine heuristic by
experiencing the whole process of machine or human recommendation system. The
results demonstrated that participants (N = 89) showed a more positive attitude
toward a machine recommender than a human recommender, even the recommended
videos contain inappropriate content. While participants who have a high level
of trust in machines exhibited a negative attitude toward recommendations.
Based on these results, we suggest that a phenomenon known as algorithm
aversion might be simultaneously considered with machine heuristic in
investigating human interaction with a machine.
