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Deep-PLNets for Dyad Ranking (Ma)

Topic for a master's thesis

Short Description:

Label ranking is a specific type of preference learning problem, namely the problem of learning a model that maps instances to rankings over a finite set of predefined (choice) alternatives [1]. These alternatives are identified by their name or label while not being characterized in terms of any properties or attributes that could be potentially useful for learning. The dyad ranking problem is a generalization of the label ranking problem, where the instances and additionally also the alternatives are represented in terms of attributes [2]. Here, the key notion of a dyad refers to a combination of an instance and an alternative attribute. In [2] it was suggested to represent dyads in terms of a Kronecker product of the instance and label attributes and use these in the bilinear Plackett-Luce, which is an extension of a statistical model used for label ranking taking the additional information of the alternatives into account. In a subsequent work [3] the bilinear Plackett-Luce model was combined with neural networks in order to learn joint-feature representations for the dyads. This method, called PLNet, allows for learning a (highly nonlinear) joint feature representation of the dyads and shows promising empirical results. However, the considered neural network is based on a simple feed-forward multi-layer perceptron architecture, while for some applications, especially for image data, it would be more desirable to use more sophisticated network architectures such as deep convolutional networks (CNN) for learning the joint feature representation of the dyads. Accordingly, the goal of this thesis would be to endow the PLNet approach with such architectures and evaluate the performance of these extensions in empirical studies.


Solid background in machine learning, especially supervised learning (e.g., classification, regression) and experimental evaluation, programming skills (Python).


Dr. Viktor Bengs, or Prof. Eyke Hüllermeier


  • [1] Y. Zhou, Y. Liu, J. Yang, X. He and L. Liu. A taxonomy of label ranking algorithms. Journal of Computers, vol. 9, no. 3 (2014): 557-565.
  • [2] D. Schäfer and E. Hüllermeier. Dyad ranking using a bilinear Plackett-Luce model. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases: 235- 256, 2015.
  • [3] D. Schäfer and E. Hüllermeier. Dyad ranking using Plackett–Luce models based on joint feature representations. Machine Learning 107 (2018): 903-941.