Recommender Systems and Algorithmic Hate

TL;DR

This paper examines the causes and consequences of algorithmic hate in recommender systems, highlighting its negative impact and proposing future research directions to improve user-system relationships.

Contribution

It provides a comprehensive analysis of algorithmic hate in recommender systems through case studies and suggests future research avenues to mitigate this issue.

Findings

Identifies common causes of algorithmic hate.

Highlights negative consequences for users and systems.

Proposes future research directions to reduce algorithmic hate.

Abstract

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.