Rank Aggregation via Heterogeneous Thurstone Preference Models
This addresses the challenge of rank aggregation in noisy pairwise comparisons for applications like recommendation systems, though it is incremental as it builds on established Thurstone and BTL frameworks.
The paper tackles the problem of aggregating ranked data from users with varying accuracy levels by proposing the Heterogeneous Thurstone Model (HTM), which extends existing models to handle heterogeneous user populations and demonstrates superior performance over existing methods in evaluations on synthetic and real data.
We propose the Heterogeneous Thurstone Model (HTM) for aggregating ranked data, which can take the accuracy levels of different users into account. By allowing different noise distributions, the proposed HTM model maintains the generality of Thurstone's original framework, and as such, also extends the Bradley-Terry-Luce (BTL) model for pairwise comparisons to heterogeneous populations of users. Under this framework, we also propose a rank aggregation algorithm based on alternating gradient descent to estimate the underlying item scores and accuracy levels of different users simultaneously from noisy pairwise comparisons. We theoretically prove that the proposed algorithm converges linearly up to a statistical error which matches that of the state-of-the-art method for the single-user BTL model. We evaluate the proposed HTM model and algorithm on both synthetic and real data, demonstrating that it outperforms existing methods.