Group Recommendation with Noisy Subjective Preferences


Social choice theory provides a principled framework for the aggregation of individuals’ preferences in support of group decision-making and recommendation. Much of this work, however, either assumes that individuals’ subjective preferences (and thus, their votes) are correctly specified by the individuals themselves, or alternatively that the votes of individuals are noisy estimates of some underlying ground truth over rankings of alternatives. We argue that neither model appropriately addresses some of the issues which arise in the context of group-recommendation domains where individuals have subjective preferences but for some reason (e.g., the high cognitive burden, concerns about privacy, etc.) may instead vote using a noisy estimate of their subjective preference rankings. In this paper, we propose a general probabilistic framework for modeling noisy subjective preferences, and explore the accuracy and reliability of four well-studied voting rules under various noise models. Our results demonstrate that there is no single reliable method amongst the examined methods. Specifically, we observe the change in noise distribution can flip one method from being the most reliable to the least.

In Computational Intelligence (2020).