#25: Breakdown - Recommender systems impact on Platform’s content and outcomes: the role of providers and algorithm designs
Update: 2025-03-07
Description
The study aims to investigate how recommender systems shape providers’ dynamics and content offerings on platforms, and to provide insights into algorithm designs for achieving better outcomes in platform design. The study reveals that recommender systems have the potential to introduce biases in providers’ understanding of user preferences, thereby impacting the variety of offerings on platforms. Moreover, it identifies algorithm design as a critical factor, with item-based collaborative filters showcasing superior performance in contexts where customers exhibit selectivity. Conversely, user-based models prove more effective in scenarios where recommendations significantly sway user decisions, ultimately boosting sales. Authors: Mohammadi Darani, Milad, and Sina Aghaie
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