Dynamic Parameter Estimation for Mixtures of Plackett-Luce Models

Aling Liao, Zan Zhang, Chenyang Bu, Lei Li

Abstract: Traditional parameter estimation algorithms rely on static datasets whose data remain constant during program execution. However, in the real-world scenario, rank data often updates in real-time, e.g., when users perform operations, such as submitting or withdrawing rankings. This dynamic nature of rank data poses challenges for applying traditional algorithms. To address this issue, we propose parameter estimation algorithms tailored for structured partial rankings based on dynamic datasets in this paper. These dynamic datasets can be classified as extended datasets and compressed datasets. To handle each dataset type, we introduce the extension preference learning algorithm and the compression preference learning algorithm based on GMM and Elsr algorithms, respectively. These algorithms ensure a relatively consistent dataset size over time, balancing accuracy and efficiency. Experimental results conducted in this paper compare the accuracy, efficiency, and stability of various algorithms using synthetic datasets, Sushi datasets, and Irish datasets, which demonstrate the effectiveness of our proposed algorithm in real-world scenarios.