  Despite increasing reliance on personalization in digital platforms, many
algorithms that curate content or information for users have been met with
resistance. When users feel dissatisfied or harmed by recommendations, this can
lead users to hate, or feel negatively towards these personalized systems.
Algorithmic hate detrimentally impacts both users and the system, and can
result in various forms of algorithmic harm, or in extreme cases can lead to
public protests against ''the algorithm'' in question. In this work, we
summarize some of the most common causes of algorithmic hate and their negative
consequences through various case studies of personalized recommender systems.
We explore promising future directions for the RecSys research community that
could help alleviate algorithmic hate and improve the relationship between
recommender systems and their users.
