Chat with the 'For You' Algorithm: An LLM-Enhanced Chatbot for Controlling Video Recommendation Flow

DOI

The rise of short-form video platforms like TikTok, driven by algorithmic recommendations, fosters immersive flow experiences. While users value personalization and engagement, they also seek greater agency over their For You recommendations. This paper designs, prototypes, and evaluates TKGPT, an LLM-enhanced conversational interface that helps users articulate their interests and understand recommendations. Through qualitative interviews and a user study, we examine how the TKGPT influences algorithmic folk theories and the sense of agency. Findings show that users primarily use TKGPT to seek relevant videos, explain preferences, and exert control over the algorithm. The resulting For You videos better reflect user interests, enhance the understanding of algorithm, improve content relevance, and reduce feelings of exploitation. Notably, users' sense of agency is significantly associated with their improved understanding of how the algorithm works. We discuss the opportunities and challenges of using conversational user interfaces to enhance user control over video recommendations.