کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6868803 1440035 2018 24 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Angle-based models for ranking data
ترجمه فارسی عنوان
مدل های مبتنی بر زاویه برای رتبه بندی داده ها
کلمات کلیدی
رتبه بندی داده ها، استنتاج تغییرات بیزی، رتبه بندی ناقص،
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نظریه محاسباتی و ریاضیات
چکیده انگلیسی
A new class of general exponential ranking models is introduced which we label angle-based models for ranking data. A consensus score vector is assumed, which assigns scores to a set of items, where the scores reflect a consensus view of the relative preference of the items. The probability of observing a ranking is modeled to be proportional to its cosine of the angle from the consensus vector. Bayesian variational inference is employed to determine the corresponding predictive density. It can be seen from simulation experiments that the Bayesian variational inference approach not only has great computational advantage compared to the traditional MCMC, but also avoids the problem of overfitting inherent when using maximum likelihood methods. The model also works when a large number of items are ranked which is usually an NP-hard problem to find the estimate of parameters for other classes of ranking models. Model extensions to incomplete rankings and mixture models are also developed. Real data applications demonstrate that the model and extensions can handle different tasks for the analysis of ranking data.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computational Statistics & Data Analysis - Volume 121, May 2018, Pages 113-136
نویسندگان
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