Filtering Discomforting Recommendations with Large Language Models

Understand

Personalized algorithms can inadvertently expose users to discomforting recommendations, potentially triggering negative consequences.

• The subjectivity of discomfort and the black-box nature of these algorithms make it challenging to effectively identify and filter such content.
• To address this, we first conducted a formative study to understand users' practices and expectations regarding discomforting recommendation filtering.
• Then, we designed a Large Language Model (LLM)-based tool named DiscomfortFilter, which constructs an editable preference profile for a user and helps the user express filtering needs through conversation to mask discomforting preferences within the profile.