Chair of Artificial Intelligence and Machine Learning

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Mixed Dyad Ranking (Ba/Ma)

Topic for a bachelor/master's thesis

Short Description:

The contextual dyad ranking framework is a generalization of the label ranking problem, where the instances and additionally also the alternatives are represented in terms of attributes. In [1] a dyad ranking learner is proposed by maximizing the likelihood-function of a bilinear Plackett-Luce model, which is an extension of a statistical model used for label ranking taking the additional information of the attributes into account. Recently, there have been some works on mixtures of Plackett-Luce models, which have shown to give a better fit to the data [2]. However, a mixture of bilinear Plackett-Luce models was not considered so far.

The goal of the thesis is to introduce a mixture model of bilinear Plackett-Luce models and investigate the identifiability of the model parameters. As a next step, an adaptation of the famous Expectation-Maximization (EM) algorithm, which is a popular framework for fitting the parameters of parametric mixture models, should be investigated.


The requirements for the thesis include the following: Implementation of a dyad ranking mixture learner and an experimental study of its performance on synthetic as well as real-word data. In particular, its superiority over classical ranking methods should be demonstrated.


Expertise in machine learning or statistical learning theory, programming skills.


Prof. Eyke Hüllermeier or Viktor Bengs


  • [1] D. Schäfer and E. Hüllermeier. Dyad Ranking using Plackett-Luce Models based on Joint Feature Representations. Machine Learning, 107(5):903–941, 2018.
  • [2] A. Liu, Z. Zhao, C. Liao, P. Lu and L. Xia Learning Plackett-Luce mixtures from partial preferences. In Proceedings of AAAI,, vol. 127, 2019.