Rank Aggregation via Heterogeneous Thurstone Preference Models
Rank Aggregation via Heterogeneous Thurstone Preference Models
复制标题
DOI：
10.1609/aaai.v34i04.5860
复制
发表时间：
2019-12
期刊：
影响因子：
--
通讯作者：
Tao Jin;Pan Xu;Quanquan Gu;Farzad Farnoud
中科院分区：
文献类型：
--
作者：
Tao Jin;Pan Xu;Quanquan Gu;Farzad Farnoud
我们提出了异构Thurstone模型（HTM）聚合排名数据，它可以考虑到不同用户的准确性水平。通过允许不同的噪声分布，建议的HTM模型保持了瑟斯顿的原始框架的一般性，因此，也扩展了布拉德利-特里-吕斯（BTL）模型的成对比较异质人群的用户。在此框架下，我们还提出了一个排名聚合算法的基础上交替梯度下降，估计潜在的项目得分和准确性水平的不同用户同时从嘈杂的成对比较。我们从理论上证明，该算法线性收敛到一个统计误差相匹配的国家的最先进的方法的单用户BTL模型。我们评估所提出的HTM模型和算法的合成和真实的数据，证明它优于现有的方法。
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.