Misinformation in video recommendations
an exploration of Top-N recommendation algorithms
Benedikt Hornig (Universiteit Maastricht)
M.S. Pera (TU Delft - Web Information Systems)
Jan Scholtes (Universiteit Maastricht)
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Abstract
With this paper, we delve into the problem of misinformation propagation in the video recommendation domain, focusing on top-N recommendation algorithms (RAs). We evaluate a broad spectrum of RAs to probe their ability to minimize misinformation recommendations while optimizing the RAs for overall performance. The results of an empirical exploration conducted using a suite of Top-N RAs and a video recommendation dataset [1] show that certain RAs excel in both performance and misinformation handling, while others struggle in mitigating misinformation. Our findings emphasize the potential of neighbourhood-based, neural, and other advanced collaborative filtering (CF) approaches in combating misinformation and contributing to more responsible recommender systems. Inspired by our findings, we propose investigating hybrid RAs and exploring specific features influencing misinformation recommendations, to further enhance the understanding and effectiveness of mitigating misinformation in recommendation systems.
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