Recommender Systems, Ground Truth, and Preference Pollution


  • Gediminas Adomavicius University of Minnesota
  • Jesse Bockstedt Emory University
  • Shawn Curley University of Minnesota
  • Jingjing Zhang Indiana University



Interactions between individuals and recommender systems can be viewed as a continuous feedback loop, consisting of pre-consumption and post-consumption phases. Pre-consumption, systems provide recommendations that are typically based on predictions of user preferences. They represent a valuable service for both providers and users as decision aids. After item consumption, the user provides post-consumption feedback (e.g., a preference rating) to the system, often used to improve the system's subsequent recommendations, completing the feedback loop. There is a growing understanding that this feedback loop can be a significant source of unintended consequences, introducing decision-making biases that can affect the quality of the “ground truth” preference data, which serves as the key input to modern recommender systems. This paper highlights two forms of bias that recommender systems inherently inflict on the “ground truth” preference data collected from users after item consumption: non-representativeness of such preference data and so-called “preference pollution,” which denotes an unintended relationship between system recommendations and the user's post-consumption preference ratings. We provide an overview of these issues and their importance for the design and application of next-generation recommendation systems, including directions for future research.




How to Cite

Adomavicius, G. ., Bockstedt, J. ., Curley, S. ., & Zhang, J. . (2022). Recommender Systems, Ground Truth, and Preference Pollution. AI Magazine, 43(2), 177-189.



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